add links

app.py

@@ -589,39 +589,20 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
 
         os.makedirs(os.path.dirname(dump_video_path), exist_ok=True)
 
+        return rgb, dump_video_path
+
+    def core_fn_export(image, video_params, working_dir):
+        rgb, dump_video_path = core_fn(image=image, video_params=video_params, working_dir=working_dir)
+        
         images_to_video(
             rgb,
             output_path=dump_video_path,
-            fps=render_fps,
+            fps=30,
             gradio_codec=False,
             verbose=True,
         )
-
-        # self.infer_single(
-        #     image_path,
-        #     motion_seqs_dir=motion_seqs_dir,
-        #     motion_img_dir=None,
-        #     motion_video_read_fps=30,
-        #     export_video=False,
-        #     export_mesh=False,
-        #     dump_tmp_dir=dump_image_dir,
-        #     dump_image_dir=dump_image_dir,
-        #     dump_video_path=dump_video_path,
-        #     shape_param=shape_pose.beta,
-        # )
-
-        # status = spaces.GPU(infer_impl(
-        #     gradio_demo_image=image_raw, 
-        #     gradio_motion_file=smplx_params_dir, 
-        #     gradio_masked_image=dump_image_path, 
-        #     gradio_video_save_path=dump_video_path
-        # ))
-
+        
         return dump_image_path, dump_video_path
-        # if status:
-        #     return dump_image_path, dump_video_path
-        # else:
-        #     return None, None
 
     _TITLE = '''LHM: Large Animatable Human Model'''
 
@@ -643,6 +624,23 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
             </div>
             """
         )
+        gr.Markdown(
+                """
+                <p align="center">
+                <a title="Website" href="https://lingtengqiu.github.io/LHM/" 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://arxiv.org/pdf/2503.10625" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                    <img src="https://www.obukhov.ai/img/badges/badge-pdf.svg">
+                </a>
+                <a title="Github" href="https://github.com/aigc3d/LHM" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                    <img src="https://img.shields.io/github/stars/aigc3d/LHM?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="badge-github-stars">
+                </a>
+                <a title="Video" href="https://www.youtube.com/watch?v=tivEpz_yiEo" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
+                    <img src="https://img.shields.io/badge/YouTube-QiuLingteng-red?logo=youtube" alt="Video">
+                </a>
+            """
+        )
         gr.HTML(
             """<p><h4 style="color: red;"> Notes: Please input full-body image in case of detection errors. We simplify the pipeline in spaces: 1) using Rembg instead of SAM2; 2) limit the output video length to 10s; For best visual quality, try the inference code on Github instead.</h4></p>"""
         )
@@ -730,7 +728,7 @@ def demo_lhm(pose_estimator, face_detector, lhm, cfg):
             outputs=[working_dir],
             queue=False,
         ).success(
-            fn=core_fn,
+            fn=core_fn_export,
             inputs=[input_image, video_input, working_dir], # video_params refer to smpl dir
             outputs=[processed_image, output_video],
         )