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app_fine.py

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+import gradio as gr
+import spaces
+from gradio_litmodel3d import LitModel3D
+
+import os
+import shutil
+os.environ['SPCONV_ALGO'] = 'native'
+from typing import *
+import torch
+import numpy as np
+import imageio
+from easydict import EasyDict as edict
+from PIL import Image
+from trellis.pipelines import TrellisVGGTTo3DPipeline
+from trellis.representations import Gaussian, MeshExtractResult
+from trellis.utils import render_utils, postprocessing_utils
+
+from wheels.vggt.vggt.utils.load_fn import load_and_preprocess_images
+from wheels.vggt.vggt.utils.pose_enc import pose_encoding_to_extri_intri
+import open3d as o3d
+from torchvision import transforms as TF
+from PIL import Image
+import sys
+sys.path.append("wheels")
+from wheels.mast3r.model import AsymmetricMASt3R
+from wheels.mast3r.fast_nn import fast_reciprocal_NNs
+from wheels.dust3r.dust3r.inference import inference
+from wheels.dust3r.dust3r.utils.image import load_images_new
+from trellis.utils.general_utils import *
+import copy
+
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+# TMP_DIR = "tmp/Trellis-demo"
+# os.environ['GRADIO_TEMP_DIR'] = 'tmp'
+os.makedirs(TMP_DIR, exist_ok=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+def start_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(user_dir, exist_ok=True)
+    
+    
+def end_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    shutil.rmtree(user_dir)
+
+@spaces.GPU
+def preprocess_image(image: Image.Image) -> Image.Image:
+    """
+    Preprocess the input image for 3D generation.
+    
+    This function is called when a user uploads an image or selects an example.
+    It applies background removal and other preprocessing steps necessary for
+    optimal 3D model generation.
+
+    Args:
+        image (Image.Image): The input image from the user
+
+    Returns:
+        Image.Image: The preprocessed image ready for 3D generation
+    """
+    processed_image = pipeline.preprocess_image(image)
+    return processed_image
+
+@spaces.GPU
+def preprocess_videos(video: str) -> List[Tuple[Image.Image, str]]:
+    """
+    Preprocess the input video for multi-image 3D generation.
+    
+    This function is called when a user uploads a video.
+    It extracts frames from the video and processes each frame to prepare them
+    for the multi-image 3D generation pipeline.
+    
+    Args:
+        video (str): The path to the input video file
+        
+    Returns:
+        List[Tuple[Image.Image, str]]: The list of preprocessed images ready for 3D generation
+    """
+    vid = imageio.get_reader(video, 'ffmpeg')
+    fps = vid.get_meta_data()['fps']
+    images = []
+    for i, frame in enumerate(vid):
+        if i % max(int(fps * 1), 1) == 0:
+            img = Image.fromarray(frame)
+            W, H = img.size
+            img = img.resize((int(W / H * 512), 512))
+            images.append(img)
+    vid.close()
+    processed_images = [pipeline.preprocess_image(image) for image in images]
+    return processed_images
+
+@spaces.GPU
+def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
+    """
+    Preprocess a list of input images for multi-image 3D generation.
+    
+    This function is called when users upload multiple images in the gallery.
+    It processes each image to prepare them for the multi-image 3D generation pipeline.
+    
+    Args:
+        images (List[Tuple[Image.Image, str]]): The input images from the gallery
+        
+    Returns:
+        List[Image.Image]: The preprocessed images ready for 3D generation
+    """
+    images = [image[0] for image in images]
+    processed_images = [pipeline.preprocess_image(image) for image in images]
+    return processed_images
+
+
+def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
+    return {
+        'gaussian': {
+            **gs.init_params,
+            '_xyz': gs._xyz.cpu().numpy(),
+            '_features_dc': gs._features_dc.cpu().numpy(),
+            '_scaling': gs._scaling.cpu().numpy(),
+            '_rotation': gs._rotation.cpu().numpy(),
+            '_opacity': gs._opacity.cpu().numpy(),
+        },
+        'mesh': {
+            'vertices': mesh.vertices.cpu().numpy(),
+            'faces': mesh.faces.cpu().numpy(),
+        },
+    }
+    
+    
+def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
+    gs = Gaussian(
+        aabb=state['gaussian']['aabb'],
+        sh_degree=state['gaussian']['sh_degree'],
+        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
+        scaling_bias=state['gaussian']['scaling_bias'],
+        opacity_bias=state['gaussian']['opacity_bias'],
+        scaling_activation=state['gaussian']['scaling_activation'],
+    )
+    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
+    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
+    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
+    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
+    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
+    
+    mesh = edict(
+        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
+        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
+    )
+    
+    return gs, mesh
+
+
+def get_seed(randomize_seed: bool, seed: int) -> int:
+    """
+    Get the random seed for generation.
+    
+    This function is called by the generate button to determine whether to use
+    a random seed or the user-specified seed value.
+    
+    Args:
+        randomize_seed (bool): Whether to generate a random seed
+        seed (int): The user-specified seed value
+        
+    Returns:
+        int: The seed to use for generation
+    """
+    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
+
+def align_camera(num_frames, extrinsic, intrinsic, rend_extrinsics, rend_intrinsics):
+
+    extrinsic_tmp = extrinsic.clone()
+    camera_relative = torch.matmul(extrinsic_tmp[:num_frames,:3,:3].permute(0,2,1), extrinsic_tmp[num_frames:,:3,:3])
+    camera_relative_angle = torch.acos(((camera_relative[:,0,0] + camera_relative[:,1,1] + camera_relative[:,2,2] - 1) / 2).clamp(-1, 1))
+    idx = torch.argmin(camera_relative_angle)
+    target_extrinsic = rend_extrinsics[idx:idx+1].clone()
+
+    focal_x = intrinsic[:num_frames,0,0].mean()
+    focal_y = intrinsic[:num_frames,1,1].mean()
+    focal = (focal_x + focal_y) / 2
+    rend_focal = (rend_intrinsics[0][0,0] + rend_intrinsics[0][1,1]) * 518 / 2
+    focal_scale = rend_focal / focal
+    target_intrinsic = intrinsic[num_frames:].clone()
+    fxy = (target_intrinsic[:,0,0] + target_intrinsic[:,1,1]) / 2 * focal_scale 
+    target_intrinsic[:,0,0] = fxy
+    target_intrinsic[:,1,1] = fxy
+    return target_extrinsic, target_intrinsic
+
+def refine_pose_mast3r(rend_image_pil, target_image_pil, original_size, fxy, target_extrinsic, rend_depth):
+    images_mast3r = load_images_new([rend_image_pil, target_image_pil], size=512, square_ok=True)
+    with torch.no_grad():
+        output = inference([tuple(images_mast3r)], mast3r_model, device, batch_size=1, verbose=False)
+    view1, pred1 = output['view1'], output['pred1']
+    view2, pred2 = output['view2'], output['pred2']
+    del output
+    desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()
+
+    # find 2D-2D matches between the two images
+    matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,
+                                                device=device, dist='dot', block_size=2**13)
+
+    # ignore small border around the edge
+    H0, W0 = view1['true_shape'][0]
+    
+    valid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < int(W0) - 3) & (
+        matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < int(H0) - 3)
+
+    H1, W1 = view2['true_shape'][0]
+    valid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < int(W1) - 3) & (
+        matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < int(H1) - 3)
+
+    valid_matches = valid_matches_im0 & valid_matches_im1
+    matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches]
+    scale_x = original_size[1] / W0.item()
+    scale_y = original_size[0] / H0.item()
+    for pixel in matches_im1:
+        pixel[0] *= scale_x
+        pixel[1] *= scale_y
+    for pixel in matches_im0:
+        pixel[0] *= scale_x
+        pixel[1] *= scale_y
+    depth_map = rend_depth[0]
+    fx, fy, cx, cy = fxy.item(), fxy.item(), original_size[1]/2, original_size[0]/2  # Example values for focal lengths and principal point
+    K = np.array([
+        [fx, 0, cx],
+        [0, fy, cy],
+        [0, 0, 1]
+    ])
+    dist_eff = np.array([0,0,0,0], dtype=np.float32)
+    predict_c2w_ini = np.linalg.inv(target_extrinsic[0].cpu().numpy())
+    predict_w2c_ini = target_extrinsic[0].cpu().numpy()
+    initial_rvec, _ = cv2.Rodrigues(predict_c2w_ini[:3,:3].astype(np.float32))
+    initial_tvec = predict_c2w_ini[:3,3].astype(np.float32)
+    K_inv = np.linalg.inv(K)
+    height, width = depth_map.shape
+    x_coords, y_coords = np.meshgrid(np.arange(width), np.arange(height))
+    x_flat = x_coords.flatten()
+    y_flat = y_coords.flatten()
+    depth_flat = depth_map.flatten()
+    x_normalized = (x_flat - K[0, 2]) / K[0, 0]
+    y_normalized = (y_flat - K[1, 2]) / K[1, 1]
+    X_camera = depth_flat * x_normalized
+    Y_camera = depth_flat * y_normalized
+    Z_camera = depth_flat
+    points_camera = np.vstack((X_camera, Y_camera, Z_camera, np.ones_like(X_camera)))
+    points_world = predict_c2w_ini @ points_camera
+    X_world = points_world[0, :]
+    Y_world = points_world[1, :]
+    Z_world = points_world[2, :]
+    points_3D = np.vstack((X_world, Y_world, Z_world))
+    scene_coordinates_gs = points_3D.reshape(3, original_size[0], original_size[1])
+    points_3D_at_pixels = np.zeros((matches_im0.shape[0], 3))
+    for i, (x, y) in enumerate(matches_im0):
+        points_3D_at_pixels[i] = scene_coordinates_gs[:, y, x]
+
+    success, rvec, tvec, inliers = cv2.solvePnPRansac(points_3D_at_pixels.astype(np.float32), matches_im1.astype(np.float32), K, \
+                                                      dist_eff,rvec=initial_rvec,tvec=initial_tvec, useExtrinsicGuess=True, reprojectionError=1.0,\
+                                                      iterationsCount=2000,flags=cv2.SOLVEPNP_EPNP)
+    R = perform_rodrigues_transformation(rvec)
+    trans = -R.T @ np.matrix(tvec)
+    predict_c2w_refine = np.eye(4)
+    predict_c2w_refine[:3,:3] = R.T
+    predict_c2w_refine[:3,3] = trans.reshape(3)
+    target_extrinsic_final = torch.tensor(predict_c2w_refine).inverse().cuda()[None].float()
+    return target_extrinsic_final
+
+def pointcloud_registration(rend_image_pil, target_image_pil, original_size, fxy, target_extrinsic, rend_depth, target_pointmap):
+    images_mast3r = load_images_new([rend_image_pil, target_image_pil], size=512, square_ok=True)
+    with torch.no_grad():
+        output = inference([tuple(images_mast3r)], mast3r_model, device, batch_size=1, verbose=False)
+    view1, pred1 = output['view1'], output['pred1']
+    view2, pred2 = output['view2'], output['pred2']
+    del output
+    desc1, desc2 = pred1['desc'].squeeze(0).detach(), pred2['desc'].squeeze(0).detach()
+
+    # find 2D-2D matches between the two images
+    matches_im0, matches_im1 = fast_reciprocal_NNs(desc1, desc2, subsample_or_initxy1=8,
+                                                device=device, dist='dot', block_size=2**13)
+
+    # ignore small border around the edge
+    H0, W0 = view1['true_shape'][0]
+    
+    valid_matches_im0 = (matches_im0[:, 0] >= 3) & (matches_im0[:, 0] < int(W0) - 3) & (
+        matches_im0[:, 1] >= 3) & (matches_im0[:, 1] < int(H0) - 3)
+
+    H1, W1 = view2['true_shape'][0]
+    valid_matches_im1 = (matches_im1[:, 0] >= 3) & (matches_im1[:, 0] < int(W1) - 3) & (
+        matches_im1[:, 1] >= 3) & (matches_im1[:, 1] < int(H1) - 3)
+
+    valid_matches = valid_matches_im0 & valid_matches_im1
+    matches_im0, matches_im1 = matches_im0[valid_matches], matches_im1[valid_matches]
+    scale_x = original_size[1] / W0.item()
+    scale_y = original_size[0] / H0.item()
+    for pixel in matches_im1:
+        pixel[0] *= scale_x
+        pixel[1] *= scale_y
+    for pixel in matches_im0:
+        pixel[0] *= scale_x
+        pixel[1] *= scale_y
+    depth_map = rend_depth[0]
+    fx, fy, cx, cy = fxy.item(), fxy.item(), original_size[1]/2, original_size[0]/2  # Example values for focal lengths and principal point
+    K = np.array([
+        [fx, 0, cx],
+        [0, fy, cy],
+        [0, 0, 1]
+    ])
+    dist_eff = np.array([0,0,0,0], dtype=np.float32)
+    predict_c2w_ini = np.linalg.inv(target_extrinsic[0].cpu().numpy())
+    predict_w2c_ini = target_extrinsic[0].cpu().numpy()
+    initial_rvec, _ = cv2.Rodrigues(predict_c2w_ini[:3,:3].astype(np.float32))
+    initial_tvec = predict_c2w_ini[:3,3].astype(np.float32)
+    K_inv = np.linalg.inv(K)
+    height, width = depth_map.shape
+    x_coords, y_coords = np.meshgrid(np.arange(width), np.arange(height))
+    x_flat = x_coords.flatten()
+    y_flat = y_coords.flatten()
+    depth_flat = depth_map.flatten()
+    x_normalized = (x_flat - K[0, 2]) / K[0, 0]
+    y_normalized = (y_flat - K[1, 2]) / K[1, 1]
+    X_camera = depth_flat * x_normalized
+    Y_camera = depth_flat * y_normalized
+    Z_camera = depth_flat
+    points_camera = np.vstack((X_camera, Y_camera, Z_camera, np.ones_like(X_camera)))
+    points_world = predict_c2w_ini @ points_camera
+    X_world = points_world[0, :]
+    Y_world = points_world[1, :]
+    Z_world = points_world[2, :]
+    points_3D = np.vstack((X_world, Y_world, Z_world))
+    scene_coordinates_gs = points_3D.reshape(3, original_size[0], original_size[1])
+    points_3D_at_pixels = np.zeros((matches_im0.shape[0], 3))
+    for i, (x, y) in enumerate(matches_im0):
+        points_3D_at_pixels[i] = scene_coordinates_gs[:, y, x]
+    
+    points_3D_at_pixels_2 = np.zeros((matches_im1.shape[0], 3))
+    for i, (x, y) in enumerate(matches_im1):
+        points_3D_at_pixels_2[i] = target_pointmap[:, y, x]
+
+    dist_1 = np.linalg.norm(points_3D_at_pixels - points_3D_at_pixels.mean(axis=0), axis=1)
+    scale_1 = dist_1[dist_1 < np.percentile(dist_1, 99)].mean()
+    dist_2 = np.linalg.norm(points_3D_at_pixels_2 - points_3D_at_pixels_2.mean(axis=0), axis=1)
+    scale_2 = dist_2[dist_2 < np.percentile(dist_2, 99)].mean()
+    # scale_1 = np.linalg.norm(points_3D_at_pixels - points_3D_at_pixels.mean(axis=0), axis=1).mean()
+    # scale_2 = np.linalg.norm(points_3D_at_pixels_2 - points_3D_at_pixels_2.mean(axis=0), axis=1).mean()
+    points_3D_at_pixels_2 = points_3D_at_pixels_2 * (scale_1 / scale_2)
+    pcd_1 = o3d.geometry.PointCloud()
+    pcd_1.points = o3d.utility.Vector3dVector(points_3D_at_pixels)
+    pcd_2 = o3d.geometry.PointCloud()
+    pcd_2.points = o3d.utility.Vector3dVector(points_3D_at_pixels_2)
+    indices = np.arange(points_3D_at_pixels.shape[0])
+    correspondences = np.stack([indices, indices], axis=1)
+    correspondences = o3d.utility.Vector2iVector(correspondences)
+    result = o3d.pipelines.registration.registration_ransac_based_on_correspondence(
+        pcd_2,
+        pcd_1,
+        correspondences,
+        0.03,
+        estimation_method=o3d.pipelines.registration.TransformationEstimationPointToPoint(False),
+        ransac_n=5,
+        criteria=o3d.pipelines.registration.RANSACConvergenceCriteria(10000, 10000),
+    )
+    transformation_matrix = result.transformation.copy()
+    transformation_matrix[:3,:3] = transformation_matrix[:3,:3] * (scale_1 / scale_2)
+    return transformation_matrix, result.fitness
+
+@spaces.GPU(duration=120)
+def generate_and_extract_glb(
+    multiimages: List[Tuple[Image.Image, str]],
+    seed: int,
+    ss_guidance_strength: float,
+    ss_sampling_steps: int,
+    slat_guidance_strength: float,
+    slat_sampling_steps: int,
+    multiimage_algo: Literal["multidiffusion", "stochastic"],
+    mesh_simplify: float,
+    texture_size: int,
+    refine: Literal["Yes", "No"],
+    ss_refine: Literal["noise", "deltav", "No"],
+    registration_num_frames: int,
+    trellis_stage1_lr: float, 
+    trellis_stage1_start_t: float,  
+    trellis_stage2_lr: float, 
+    trellis_stage2_start_t: float,
+    req: gr.Request,
+) -> Tuple[dict, str, str, str]:
+    """
+    Convert an image to a 3D model and extract GLB file.
+
+    Args:
+        image (Image.Image): The input image.
+        multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
+        is_multiimage (bool): Whether is in multi-image mode.
+        seed (int): The random seed.
+        ss_guidance_strength (float): The guidance strength for sparse structure generation.
+        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
+        slat_guidance_strength (float): The guidance strength for structured latent generation.
+        slat_sampling_steps (int): The number of sampling steps for structured latent generation.
+        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
+        mesh_simplify (float): The mesh simplification factor.
+        texture_size (int): The texture resolution.
+
+    Returns:
+        dict: The information of the generated 3D model.
+        str: The path to the video of the 3D model.
+        str: The path to the extracted GLB file.
+        str: The path to the extracted GLB file (for download).
+    """
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    image_files = [image[0] for image in multiimages]
+
+    # Generate 3D model
+    outputs, coords, ss_noise = pipeline.run(
+        image=image_files,
+        seed=seed,
+        formats=["gaussian", "mesh"],
+        preprocess_image=False,
+        sparse_structure_sampler_params={
+            "steps": ss_sampling_steps,
+            "cfg_strength": ss_guidance_strength,
+        },
+        slat_sampler_params={
+            "steps": slat_sampling_steps,
+            "cfg_strength": slat_guidance_strength,
+        },
+        mode=multiimage_algo,
+    )
+    if refine == "Yes":
+        try:
+            images, alphas = load_and_preprocess_images(multiimages)
+            images, alphas = images.to(device), alphas.to(device)
+            with torch.no_grad():
+                with torch.cuda.amp.autocast(dtype=pipeline.VGGT_dtype):
+                    images = images[None]
+                    aggregated_tokens_list, ps_idx = pipeline.VGGT_model.aggregator(images)
+                # Predict Cameras
+                pose_enc = pipeline.VGGT_model.camera_head(aggregated_tokens_list)[-1]
+                # Extrinsic and intrinsic matrices, following OpenCV convention (camera from world)
+                extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, images.shape[-2:])
+                # Predict Point Cloud
+                point_map, point_conf = pipeline.VGGT_model.point_head(aggregated_tokens_list, images, ps_idx)
+                del aggregated_tokens_list
+                mask = (alphas[:,0,...][...,None] > 0.8)
+                conf_threshold = np.percentile(point_conf.cpu().numpy(), 50)
+                confidence_mask = (point_conf[0] > conf_threshold) & (point_conf[0] > 1e-5)
+                mask = mask & confidence_mask[...,None]
+                point_map_by_unprojection = point_map[0]
+                point_map_clean = point_map_by_unprojection[mask[...,0]]
+                center_point = point_map_clean.mean(0)
+                scale = np.percentile((point_map_clean - center_point[None]).norm(dim=-1).cpu().numpy(), 98)
+                outlier_mask = (point_map_by_unprojection - center_point[None]).norm(dim=-1) <= scale
+                final_mask = mask & outlier_mask[...,None]
+                point_map_perframe = (point_map_by_unprojection - center_point[None, None, None]) / (2 * scale)
+                point_map_perframe[~final_mask[...,0]] = 127/255
+                point_map_perframe = point_map_perframe.permute(0,3,1,2)
+                images = images[0].permute(0,2,3,1)
+                images[~(alphas[:,0,...][...,None] > 0.8)[...,0]] = 0.
+                input_images = images.permute(0,3,1,2).clone()
+                vggt_extrinsic = extrinsic[0]
+                vggt_extrinsic = torch.cat([vggt_extrinsic, torch.tensor([[[0,0,0,1]]]).repeat(vggt_extrinsic.shape[0], 1, 1).to(vggt_extrinsic)], dim=1)
+                vggt_intrinsic = intrinsic[0]
+                vggt_intrinsic[:,:2] = vggt_intrinsic[:,:2] / 518
+                vggt_extrinsic[:,:3,3] = (torch.matmul(vggt_extrinsic[:,:3,:3], center_point[None,:,None].float())[...,0] + vggt_extrinsic[:,:3,3]) / (2 * scale)
+                pointcloud = point_map_perframe.permute(0,2,3,1)[final_mask[...,0]]
+                idxs = torch.randperm(pointcloud.shape[0])[:min(50000, pointcloud.shape[0])]
+                pcd = o3d.geometry.PointCloud()
+                pcd.points = o3d.utility.Vector3dVector(pointcloud[idxs].cpu().numpy())
+                cl, ind = pcd.remove_statistical_outlier(nb_neighbors=30, std_ratio=3.0)
+                inlier_cloud = pcd.select_by_index(ind)
+                outlier_cloud = pcd.select_by_index(ind, invert=True)
+                voxel_size = 1/64
+                down_pcd = inlier_cloud.voxel_down_sample(voxel_size)
+            torch.cuda.empty_cache()
+
+            video, rend_extrinsics, rend_intrinsics = render_utils.render_multiview(outputs['gaussian'][0], num_frames=registration_num_frames)
+            rend_extrinsics = torch.stack(rend_extrinsics, dim=0)
+            rend_intrinsics = torch.stack(rend_intrinsics, dim=0)
+            target_extrinsics = []
+            target_intrinsics = []
+            target_transforms = []
+            target_fitnesses = []   
+            for k in range(len(image_files)):
+                images = torch.stack([TF.ToTensor()(render_image) for render_image in video['color']] + [TF.ToTensor()(image_files[k].convert("RGB"))], dim=0)
+                # if len(images) == 0:
+                with torch.no_grad():
+                    with torch.cuda.amp.autocast(dtype=pipeline.VGGT_dtype):
+                        # predictions = vggt_model(images.cuda())
+                        aggregated_tokens_list, ps_idx = pipeline.VGGT_model.aggregator(images[None].cuda())
+                    pose_enc = pipeline.VGGT_model.camera_head(aggregated_tokens_list)[-1]
+                extrinsic, intrinsic = pose_encoding_to_extri_intri(pose_enc, images.shape[-2:])
+                extrinsic, intrinsic = extrinsic[0], intrinsic[0]
+                extrinsic = torch.cat([extrinsic, torch.tensor([0,0,0,1])[None,None].repeat(extrinsic.shape[0], 1, 1).to(extrinsic.device)], dim=1)
+                del aggregated_tokens_list, ps_idx
+
+                target_extrinsic, target_intrinsic = align_camera(registration_num_frames, extrinsic, intrinsic, rend_extrinsics, rend_intrinsics)
+                fxy = target_intrinsic[:,0,0]
+                target_intrinsic_tmp = target_intrinsic.clone()
+                target_intrinsic_tmp[:,:2] = target_intrinsic_tmp[:,:2] / 518
+
+                target_extrinsic_list = [target_extrinsic]
+                iou_list = []
+                iterations = 3
+                for i in range(iterations + 1):
+                    j = 0
+                    rend = render_utils.render_frames(outputs['gaussian'][0], target_extrinsic, target_intrinsic_tmp, {'resolution': 518, 'bg_color': (0, 0, 0)}, need_depth=True)
+                    rend_image = rend['color'][j] # (518, 518, 3)
+                    rend_depth = rend['depth'][j] # (3, 518, 518)
+
+                    depth_single = rend_depth[0].astype(np.float32)   # (H, W)
+                    mask = (depth_single != 0).astype(np.uint8)  # 
+                    kernel = np.ones((3, 3), np.uint8)
+                    mask_eroded = cv2.erode(mask, kernel, iterations=3)
+                    depth_eroded = depth_single * mask_eroded
+                    rend_depth_eroded = np.stack([depth_eroded]*3, axis=0)
+
+                    rend_image = torch.tensor(rend_image).permute(2,0,1) / 255
+                    target_image = images[registration_num_frames:].to(target_extrinsic.device)[j]
+                    original_size = (rend_image.shape[1], rend_image.shape[2])
+                    
+                    import torchvision
+                    torchvision.utils.save_image(rend_image, 'rend_image_{}.png'.format(k))
+                    torchvision.utils.save_image(target_image, 'target_image_{}.png'.format(k))
+                    
+                    mask_rend = (rend_image.detach().cpu() > 0).any(dim=0)
+                    mask_target = (target_image.detach().cpu() > 0).any(dim=0)
+                    intersection = (mask_rend & mask_target).sum().item()
+                    union = (mask_rend | mask_target).sum().item()
+                    iou = intersection / union if union > 0 else 0.0
+                    iou_list.append(iou)
+
+                    if i == iterations:
+                        break
+
+                    rend_image = rend_image * torch.from_numpy(mask_eroded[None]).to(rend_image.device)
+                    rend_image_pil = Image.fromarray((rend_image.permute(1,2,0).cpu().numpy() * 255).astype(np.uint8))
+                    target_image_pil = Image.fromarray((target_image.permute(1,2,0).cpu().numpy() * 255).astype(np.uint8))
+                    target_extrinsic[j:j+1] = refine_pose_mast3r(rend_image_pil, target_image_pil, original_size, fxy[j:j+1], target_extrinsic[j:j+1], rend_depth_eroded)    
+                    target_extrinsic_list.append(target_extrinsic[j:j+1])
+                
+                idx = iou_list.index(max(iou_list))
+                target_extrinsic[j:j+1] = target_extrinsic_list[idx]
+                target_transform, fitness = pointcloud_registration(rend_image_pil, target_image_pil, original_size, fxy[j:j+1], target_extrinsic[j:j+1], rend_depth_eroded, point_map_perframe[k].cpu().numpy())
+                target_transforms.append(target_transform)
+                target_fitnesses.append(fitness)
+                
+                target_extrinsics.append(target_extrinsic[j:j+1])
+                target_intrinsics.append(target_intrinsic_tmp[j:j+1])
+            target_extrinsics = torch.cat(target_extrinsics, dim=0)
+            target_intrinsics = torch.cat(target_intrinsics, dim=0)
+            
+            target_fitnesses_filtered = [x for x in target_fitnesses if x < 1]
+            idx = target_fitnesses.index(max(target_fitnesses_filtered))
+            target_transform = target_transforms[idx]
+            down_pcd_align = copy.deepcopy(down_pcd).transform(target_transform)
+            pcd = o3d.geometry.PointCloud()
+            pcd.points = o3d.utility.Vector3dVector(coords[:,1:].cpu().numpy() / 64 - 0.5)
+            reg_p2p = o3d.pipelines.registration.registration_icp(
+                down_pcd_align, pcd, 0.01, np.eye(4),
+                o3d.pipelines.registration.TransformationEstimationPointToPoint(with_scaling=True),
+                o3d.pipelines.registration.ICPConvergenceCriteria(max_iteration = 10000))
+            down_pcd_align_2 = copy.deepcopy(down_pcd_align).transform(reg_p2p.transformation)
+            input_points = torch.tensor(np.asarray(down_pcd_align_2.points)).to(extrinsic.device).float()
+            input_points = ((input_points + 0.5).clip(0, 1) * 63).to(torch.int32)
+            
+            outputs = pipeline.run_refine(
+                image=image_files,
+                ss_learning_rate=trellis_stage1_lr,
+                ss_start_t=trellis_stage1_start_t,
+                apperance_learning_rate=trellis_stage2_lr,
+                apperance_start_t=trellis_stage2_start_t,
+                extrinsics=target_extrinsics,
+                intrinsics=target_intrinsics,
+                ss_noise=ss_noise,
+                input_points=input_points,
+                ss_refine_type = ss_refine,
+                coords=coords if ss_refine == "No" else None,
+                seed=seed,
+                formats=["mesh", "gaussian"],
+                sparse_structure_sampler_params={
+                    "steps": ss_sampling_steps,
+                    "cfg_strength": ss_guidance_strength,
+                },
+                slat_sampler_params={
+                    "steps": slat_sampling_steps,
+                    "cfg_strength": slat_guidance_strength,
+                },
+                mode=multiimage_algo,
+            )
+        except Exception as e:
+            print(f"Error during refinement: {e}")
+    # Render video
+    # import uuid
+    # output_id = str(uuid.uuid4())
+    # os.makedirs(f"{TMP_DIR}/{output_id}", exist_ok=True)
+    # video_path = f"{TMP_DIR}/{output_id}/preview.mp4"
+    # glb_path = f"{TMP_DIR}/{output_id}/mesh.glb"
+    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
+    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
+    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
+    video_path = os.path.join(user_dir, 'sample.mp4')
+    imageio.mimsave(video_path, video, fps=15)
+    
+    # Extract GLB
+    gs = outputs['gaussian'][0]
+    mesh = outputs['mesh'][0]
+    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
+    glb_path = os.path.join(user_dir, 'sample.glb')
+    glb.export(glb_path)
+    
+    # Pack state for optional Gaussian extraction
+    state = pack_state(gs, mesh)
+    
+    torch.cuda.empty_cache()
+    return state, video_path, glb_path, glb_path
+
+
+@spaces.GPU
+def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
+    """
+    Extract a Gaussian splatting file from the generated 3D model.
+    
+    This function is called when the user clicks "Extract Gaussian" button.
+    It converts the 3D model state into a .ply file format containing
+    Gaussian splatting data for advanced 3D applications.
+
+    Args:
+        state (dict): The state of the generated 3D model containing Gaussian data
+        req (gr.Request): Gradio request object for session management
+
+    Returns:
+        Tuple[str, str]: Paths to the extracted Gaussian file (for display and download)
+    """
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    gs, _ = unpack_state(state)
+    gaussian_path = os.path.join(user_dir, 'sample.ply')
+    gs.save_ply(gaussian_path)
+    torch.cuda.empty_cache()
+    return gaussian_path, gaussian_path
+
+
+def prepare_multi_example() -> List[Image.Image]:
+    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
+    images = []
+    for case in multi_case:
+        _images = []
+        for i in range(1, 9):
+            if os.path.exists(f'assets/example_multi_image/{case}_{i}.png'):
+                img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
+                W, H = img.size
+                img = img.resize((int(W / H * 512), 512))
+                _images.append(np.array(img))
+        if len(_images) > 0:
+            images.append(Image.fromarray(np.concatenate(_images, axis=1)))
+    return images
+
+
+def split_image(image: Image.Image) -> List[Image.Image]:
+    """
+    Split a multi-view image into separate view images.
+    
+    This function is called when users select multi-image examples that contain
+    multiple views in a single concatenated image. It automatically splits them
+    based on alpha channel boundaries and preprocesses each view.
+    
+    Args:
+        image (Image.Image): A concatenated image containing multiple views
+        
+    Returns:
+        List[Image.Image]: List of individual preprocessed view images
+    """
+    image = np.array(image)
+    alpha = image[..., 3]
+    alpha = np.any(alpha>0, axis=0)
+    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
+    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
+    images = []
+    for s, e in zip(start_pos, end_pos):
+        images.append(Image.fromarray(image[:, s:e+1]))
+    return [preprocess_image(image) for image in images]
+
+# Create interface
+demo = gr.Blocks(
+    title="ReconViaGen",
+    css="""
+        .slider .inner { width: 5px; background: #FFF; }
+        .viewport { aspect-ratio: 4/3; }
+        .tabs button.selected { font-size: 20px !important; color: crimson !important; }
+        h1, h2, h3 { text-align: center; display: block; }
+        .md_feedback li { margin-bottom: 0px !important; }
+    """
+)
+with demo:
+    gr.Markdown("""
+    # 💻 ReconViaGen
+    <p align="center">
+    <a title="Github" href="https://github.com/GAP-LAB-CUHK-SZ/ReconViaGen" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://img.shields.io/github/stars/GAP-LAB-CUHK-SZ/ReconViaGen?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
+    </a>
+    <a title="Website" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://www.obukhov.ai/img/badges/badge-website.svg">
+    </a>
+    <a title="arXiv" href="https://jiahao620.github.io/reconviagen/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+        <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
+    </a>
+    </p>
+    
+    ✨This demo is partial. We will release the whole model later. Stay tuned!✨
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            with gr.Tabs() as input_tabs:
+                with gr.Tab(label="Input Video or Images", id=0) as multiimage_input_tab:
+                    input_video = gr.Video(label="Upload Video", interactive=True, height=300)
+                    image_prompt = gr.Image(label="Image Prompt", format="png", visible=False, image_mode="RGBA", type="pil", height=300)
+                    multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
+                    gr.Markdown("""
+                        Input different views of the object in separate images. 
+                    """)
+        
+            with gr.Accordion(label="Generation Settings", open=False):
+                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=False)
+                gr.Markdown("Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=30, step=1)
+                gr.Markdown("Stage 2: Structured Latent Generation")
+                with gr.Row():
+                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
+                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="multidiffusion")
+                refine = gr.Radio(["Yes", "No"], label="Refinement of Not", value="Yes")
+                ss_refine = gr.Radio(["noise", "deltav", "No"], label="Sparse Structure refinement of not", value="No")
+                registration_num_frames = gr.Slider(20, 50, label="Number of frames in registration", value=30, step=1)
+                trellis_stage1_lr = gr.Slider(1e-4, 1., label="trellis_stage1_lr", value=1e-1, step=5e-4)
+                trellis_stage1_start_t = gr.Slider(0., 1., label="trellis_stage1_start_t", value=0.5, step=0.01)
+                trellis_stage2_lr = gr.Slider(1e-4, 1., label="trellis_stage2_lr", value=1e-1, step=5e-4)
+                trellis_stage2_start_t = gr.Slider(0., 1., label="trellis_stage2_start_t", value=0.5, step=0.01)
+
+            with gr.Accordion(label="GLB Extraction Settings", open=False):
+                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
+                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
+
+            generate_btn = gr.Button("Generate & Extract GLB", variant="primary")
+            extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
+            gr.Markdown("""
+                        *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
+                        """)
+
+        with gr.Column():
+            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
+            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
+            
+            with gr.Row():
+                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
+                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
+    
+    output_buf = gr.State()
+
+    # Example images at the bottom of the page
+    with gr.Row() as multiimage_example:
+        examples_multi = gr.Examples(
+            examples=prepare_multi_example(),
+            inputs=[image_prompt],
+            fn=split_image,
+            outputs=[multiimage_prompt],
+            run_on_click=True,
+            examples_per_page=8,
+        )
+
+    # Handlers
+    demo.load(start_session)
+    demo.unload(end_session)
+
+    input_video.upload(
+        preprocess_videos,
+        inputs=[input_video],
+        outputs=[multiimage_prompt],
+    )
+    input_video.clear(
+        lambda: tuple([None, None]),
+        outputs=[input_video, multiimage_prompt],
+    )
+    multiimage_prompt.upload(
+        preprocess_images,
+        inputs=[multiimage_prompt],
+        outputs=[multiimage_prompt],
+    )
+
+    generate_btn.click(
+        get_seed,
+        inputs=[randomize_seed, seed],
+        outputs=[seed],
+    ).then(
+        generate_and_extract_glb,
+        inputs=[multiimage_prompt, seed, ss_guidance_strength, ss_sampling_steps, 
+                slat_guidance_strength, slat_sampling_steps, multiimage_algo, 
+                mesh_simplify, texture_size, refine, ss_refine, registration_num_frames,
+                trellis_stage1_lr, trellis_stage1_start_t, trellis_stage2_lr, 
+                trellis_stage2_start_t],
+        outputs=[output_buf, video_output, model_output, download_glb],
+    ).then(
+        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
+        outputs=[extract_gs_btn, download_glb],
+    )
+
+    video_output.clear(
+        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False), gr.Button(interactive=False)]),
+        outputs=[extract_gs_btn, download_glb, download_gs],
+    )
+    
+    extract_gs_btn.click(
+        extract_gaussian,
+        inputs=[output_buf],
+        outputs=[model_output, download_gs],
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_gs],
+    )
+
+    model_output.clear(
+        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
+        outputs=[download_glb, download_gs],
+    )
+    
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    pipeline = TrellisVGGTTo3DPipeline.from_pretrained("Stable-X/trellis-vggt-v0-2")
+    # pipeline = TrellisVGGTTo3DPipeline.from_pretrained("weights/trellis-vggt-v0-1")
+    pipeline.cuda()
+    pipeline.VGGT_model.cuda()
+    pipeline.birefnet_model.cuda()
+    pipeline.dreamsim_model.cuda()
+    mast3r_model = AsymmetricMASt3R.from_pretrained("naver/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric").cuda().eval() 
+    # mast3r_model = AsymmetricMASt3R.from_pretrained("weights/MAST3R/MASt3R_ViTLarge_BaseDecoder_512_catmlpdpt_metric.pth").cuda().eval()   
+    demo.launch()

trellis/pipelines/trellis_image_to_3d.py

@@ -19,7 +19,8 @@ from typing import *
 from scipy.spatial.transform import Rotation
 from transformers import AutoModelForImageSegmentation
 import rembg
-# from dreamsim import dreamsim
+from dreamsim import dreamsim
+from tqdm import tqdm
 
 def export_point_cloud(xyz, color):
     # Convert tensors to numpy arrays if needed
@@ -475,6 +476,76 @@ class TrellisImageTo3DPipeline(Pipeline):
 
         return coords
 
+    def sample_sparse_structure_opt_noise(
+        self,
+        cond: dict,
+        ss: torch.Tensor,
+        ss_learning_rate: float=1e-3, 
+        num_samples: int = 1,
+        sampler_params: dict = {},
+        noise: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """
+        Sample sparse structures with the given conditioning.
+        
+        Args:
+            cond (dict): The conditioning information.
+            num_samples (int): The number of samples to generate.
+            sampler_params (dict): Additional parameters for the sampler.
+        """
+        # Sample occupancy latent
+        flow_model = self.models['sparse_structure_flow_model']
+        ss = ss.float()
+        reso = flow_model.resolution
+        if noise is None:
+            noise = torch.randn(num_samples, flow_model.in_channels, reso, reso, reso).to(self.device)
+        torch.cuda.empty_cache()
+        noise = torch.nn.Parameter(noise.to(self.device))
+        optimizer = torch.optim.Adam([noise], betas=(0.5, 0.9), lr=ss_learning_rate)
+        total_steps = 5
+        def cosine_anealing(step, total_steps, start_lr, end_lr):
+            return end_lr + 0.5 * (start_lr - end_lr) * (1 + np.cos(np.pi * step / total_steps))
+        sampler_params = {**self.sparse_structure_sampler_params, **sampler_params}
+        fix_cond = cond['cond'].clone()
+        with tqdm(total=total_steps, disable=False, desc='Geometry (opt): optimizing') as pbar:
+            for step in range(total_steps):
+                optimizer.zero_grad()
+                shuffle_idx = torch.randperm(fix_cond.shape[0])
+                cond['cond'] = fix_cond[shuffle_idx]
+                norm_noise = (noise - noise.mean()) / noise.std()
+                ss_slat = self.sparse_structure_sampler.sample_opt(
+                    flow_model,
+                    norm_noise,
+                    **cond,
+                    **{**self.sparse_structure_sampler_params, **{"steps": 1, "cfg_strength": sampler_params["cfg_strength"]}},
+                    verbose=False
+                ).samples
+                ss_decoder = self.models['sparse_structure_decoder']
+                logits = F.sigmoid(ss_decoder(ss_slat))
+                loss = 1 - (2 * (logits * ss.float()).sum() + 1) / (logits.sum() + ss.float().sum() + 1)
+                # loss.backward()
+                # optimizer.step()
+                # 仅对 noise 求导，避免保留整个计算图（比 retain_graph=True 更省显存）
+                grads = torch.autograd.grad(loss, noise, retain_graph=False, allow_unused=False)[0]
+                # 把梯度写回 noise.grad 供 optimizer 使用
+                noise.grad = grads
+                optimizer.step()
+                optimizer.param_groups[0]['lr'] = cosine_anealing(step, total_steps, ss_learning_rate, 1e-5)
+                pbar.set_postfix({'loss': loss.item()})
+                pbar.update()
+        
+        noise = noise.detach()
+        torch.cuda.empty_cache()
+        z_s = self.sparse_structure_sampler.sample(
+            flow_model,
+            noise,
+            **cond,
+            **sampler_params,
+            verbose=True
+        ).samples
+        coords = torch.argwhere(ss_decoder(z_s)>0)[:, [0, 2, 3, 4]].int()
+        return coords
+
     def encode_slat(
         self,
         slat: sp.SparseTensor,
@@ -907,6 +978,7 @@ class TrellisVGGTTo3DPipeline(TrellisImageTo3DPipeline):
         intrinsics: torch.Tensor,
         ss_noise: torch.Tensor,
         input_points: torch.Tensor,
+        ss_refine_type: str = 'No',
         coords: torch.Tensor = None,
         num_samples: int = 1,
         seed: int = 42,
@@ -928,7 +1000,10 @@ class TrellisVGGTTo3DPipeline(TrellisImageTo3DPipeline):
             ss = ss[None, None]
             torch.cuda.empty_cache()
             # Sample structured latent
-            coords = self.sample_sparse_structure_opt(ss_cond, ss, ss_learning_rate, ss_start_t, num_samples, sparse_structure_sampler_params)
+            if ss_refine_type == 'noise':
+                coords = self.sample_sparse_structure_opt_noise(ss_cond, ss, ss_learning_rate, num_samples, sparse_structure_sampler_params, ss_noise)
+            elif ss_refine_type == 'deltav':
+                coords = self.sample_sparse_structure_opt(ss_cond, ss, ss_learning_rate, ss_start_t, num_samples, sparse_structure_sampler_params, ss_noise)
             torch.cuda.empty_cache()
 
         # pcd = o3d.geometry.PointCloud()
@@ -987,8 +1062,8 @@ class TrellisVGGTTo3DPipeline(TrellisImageTo3DPipeline):
 
         new_pipeline._init_image_cond_model(args['image_cond_model'])
 
-        # model, _ = dreamsim(pretrained=True, device=new_pipeline.device, dreamsim_type="dino_vitb16", cache_dir="weights/dreamsim")
-        # new_pipeline.dreamsim_model = model
-        # new_pipeline.dreamsim_model.eval()
+        model, _ = dreamsim(pretrained=True, device=new_pipeline.device, dreamsim_type="dino_vitb16", cache_dir="weights/dreamsim")
+        new_pipeline.dreamsim_model = model
+        new_pipeline.dreamsim_model.eval()
 
-        return new_pipeline
+        return new_pipeline