SceneDiffuser commited on
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  1. .gitattributes +1 -0
  2. app.py +79 -0
  3. figures/teaser.png +3 -0
.gitattributes CHANGED
@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ figures/teaser.png filter=lfs diff=lfs merge=lfs -text
app.py ADDED
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+ import os
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+ import gradio as gr
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+ import random
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+ import pickle
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+ import numpy as np
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+ from PIL import Image
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+ from huggingface_hub import hf_hub_download
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+
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+ def pose_generation(scene, count):
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+ assert isinstance(scene, str)
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+ results_path = hf_hub_download('SceneDiffuser/SceneDiffuser', 'results/pose_generation/results.pkl')
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+ with open(results_path, 'rb') as f:
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+ results = pickle.load(f)
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+
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+ images = [Image.fromarray(results[scene][random.randint(0, 19)]) for i in range(count)]
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+ return images
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+
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+ def path_planning(case_id):
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+ assert isinstance(case_id, str)
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+ results_path = hf_hub_download('SceneDiffuser/SceneDiffuser', 'results/path_planning/results.pkl')
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+ with open(results_path, 'rb') as f:
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+ results = pickle.load(f)
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+
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+ case = results[case_id]
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+ steps = case['step']
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+ image = Image.fromarray(case['image'])
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+ return image, steps
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+
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+ with gr.Blocks() as demo:
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+ gr.Markdown("# **<p align='center'>Diffusion-based Generation, Optimization, and Planning in 3D Scenes</p>**")
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+ gr.HTML(value="<img src='file/figures/teaser.png' alt='Teaser' width='710px' height='284px' style='display: block; margin: auto;'>")
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+ 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>")
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+ gr.Markdown("<p align='center'><i>\"SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning.\"</i></p>")
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+
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+ ## five task
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+ ## pose generation
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+ with gr.Tab("Pose Generation"):
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+ with gr.Row():
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+ with gr.Column():
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+ input1 = [
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+ gr.Dropdown(choices=['MPH16', 'MPH1Library', 'N0SittingBooth', 'N3OpenArea'], label='Scenes'),
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+ gr.Slider(minimum=1, maximum=4, step=1, label='Count', interactive=True)
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+ ]
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+ button1 = gr.Button("Generate")
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+ with gr.Column():
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+ output1 = [
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+ gr.Gallery(label="Result").style(grid=[1], height="auto")
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+ ]
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+ button1.click(pose_generation, inputs=input1, outputs=output1)
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+
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+ ## motion generation
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+ with gr.Tab("Motion Generation"):
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+ gr.Markdown('Coming soon!')
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+
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+ ## grasp generation
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+ with gr.Tab("Grasp Generation"):
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+ gr.Markdown('Coming soon!')
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+
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+ ## path planning
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+ with gr.Tab("Path Planing"):
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+ with gr.Row():
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+ with gr.Column():
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+ input4 = [
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+ 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'),
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+ ]
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+ button4 = gr.Button("Run")
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+ with gr.Column():
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+ # output4 = gr.Gallery(label="Result").style(grid=[1], height="auto")
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+ output4 = [
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+ gr.Image(label="Result"),
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+ gr.Number(label="Steps", precision=0)
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+ ]
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+ button4.click(path_planning, inputs=input4, outputs=output4)
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+
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+ ## arm motion planning
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+ with gr.Tab("Arm Motion Planning"):
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+ gr.Markdown('Coming soon!')
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+
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+ demo.launch()
figures/teaser.png ADDED

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