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@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+figures/teaser.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,79 @@
+import os
+import gradio as gr
+import random
+import pickle
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+
+def pose_generation(scene, count):
+    assert isinstance(scene, str)
+    results_path = hf_hub_download('SceneDiffuser/SceneDiffuser', 'results/pose_generation/results.pkl')
+    with open(results_path, 'rb') as f:
+        results = pickle.load(f)
+    
+    images = [Image.fromarray(results[scene][random.randint(0, 19)]) for i in range(count)]
+    return images
+
+def path_planning(case_id):
+    assert isinstance(case_id, str)
+    results_path = hf_hub_download('SceneDiffuser/SceneDiffuser', 'results/path_planning/results.pkl')
+    with open(results_path, 'rb') as f:
+        results = pickle.load(f)
+
+    case = results[case_id]
+    steps = case['step']
+    image = Image.fromarray(case['image'])
+    return image, steps
+
+with gr.Blocks() as demo:
+    gr.Markdown("# **<p align='center'>Diffusion-based Generation, Optimization, and Planning in 3D Scenes</p>**")
+    gr.HTML(value="<img src='file/figures/teaser.png' alt='Teaser' width='710px' height='284px' style='display: block; margin: auto;'>")
+    gr.HTML(value="<p align='center' style='font-size: 1.25em; color: #485fc7;'><a href='' target='_blank'>Paper</a> | <a href='' target='_blank'>Project Page</a> | <a href='' target='_blank'>Github</a></p>")
+    gr.Markdown("<p align='center'><i>\"SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.\"</i></p>")
+
+    ## five task
+    ## pose generation
+    with gr.Tab("Pose Generation"):
+        with gr.Row():
+            with gr.Column():
+                input1 = [
+                    gr.Dropdown(choices=['MPH16', 'MPH1Library', 'N0SittingBooth', 'N3OpenArea'], label='Scenes'),
+                    gr.Slider(minimum=1, maximum=4, step=1, label='Count', interactive=True)
+                ]
+                button1 = gr.Button("Generate")
+            with gr.Column():
+                output1 = [
+                    gr.Gallery(label="Result").style(grid=[1], height="auto")
+                ]
+    button1.click(pose_generation, inputs=input1, outputs=output1)
+
+    ## motion generation
+    with gr.Tab("Motion Generation"):
+        gr.Markdown('Coming soon!')
+    
+    ## grasp generation
+    with gr.Tab("Grasp Generation"):
+        gr.Markdown('Coming soon!')
+    
+    ## path planning
+    with gr.Tab("Path Planing"):
+        with gr.Row():
+            with gr.Column():
+                input4 = [
+                    gr.Dropdown(choices=['scene0603_00_N0pT', 'scene0621_00_cJ4H', 'scene0634_00_48Y3', 'scene0634_00_gIRH', 'scene0637_00_YgjR', 'scene0640_00_BO94', 'scene0641_00_3K6J', 'scene0641_00_KBKx', 'scene0641_00_cb7l', 'scene0645_00_35Hy', 'scene0645_00_47D1', 'scene0645_00_XfLE', 'scene0667_00_DK4F', 'scene0667_00_o7XB', 'scene0667_00_rUMp', 'scene0672_00_U250', 'scene0673_00_Jyw8', 'scene0673_00_u1lJ', 'scene0678_00_QbNL', 'scene0678_00_RrY0', 'scene0678_00_aE1p', 'scene0678_00_hnXu', 'scene0694_00_DgAL', 'scene0694_00_etF5', 'scene0698_00_tT3Q'], label='Scenes'),
+                ]
+                button4 = gr.Button("Run")
+            with gr.Column():
+                # output4 = gr.Gallery(label="Result").style(grid=[1], height="auto")
+                output4 = [
+                    gr.Image(label="Result"),
+                    gr.Number(label="Steps", precision=0)
+                ]
+    button4.click(path_planning, inputs=input4, outputs=output4)
+
+    ## arm motion planning
+    with gr.Tab("Arm Motion Planning"):
+        gr.Markdown('Coming soon!')
+    
+demo.launch()