# install import glob import gradio as gr import numpy as np import os import subprocess from apps.infer import generate_model if os.getenv('SYSTEM') == 'spaces': subprocess.run('pip install pyembree'.split()) subprocess.run( 'pip install git+https://github.com/YuliangXiu/neural_voxelization_layer.git'.split()) # running description = ''' # ICON Clothed Human Digitization ### ICON: Implicit Clothed humans Obtained from Normals (CVPR 2022) <table> <th> <ul> <li><strong>Homepage</strong> <a href="http://icon.is.tue.mpg.de">icon.is.tue.mpg.de</a></li> <li><strong>Code</strong> <a href="https://github.com/YuliangXiu/ICON">YuliangXiu/ICON</a></li> <li><strong>Paper</strong> <a href="https://arxiv.org/abs/2112.09127">arXiv</a>, <a href="https://readpaper.com/paper/4569785684533977089">ReadPaper</a></li> <li><strong>Chatroom</strong> <a href="https://discord.gg/Vqa7KBGRyk">Discord</a></li> <li><strong>Colab Notebook</strong> <a href="https://colab.research.google.com/drive/1-AWeWhPvCTBX0KfMtgtMk10uPU05ihoA?usp=sharing">Google Colab</a></li> </ul> <a href="https://twitter.com/yuliangxiu"><img alt="Twitter Follow" src="https://img.shields.io/twitter/follow/yuliangxiu?style=social"></a> <iframe src="https://ghbtns.com/github-btn.html?user=yuliangxiu&repo=ICON&type=star&count=true&v=2&size=small" frameborder="0" scrolling="0" width="100" height="20"></iframe> <a href="https://youtu.be/hZd6AYin2DE"><img alt="YouTube Video Views" src="https://img.shields.io/youtube/views/hZd6AYin2DE?style=social"></a> </th> <th> <iframe width="560" height="315" src="https://www.youtube.com/embed/hZd6AYin2DE" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </th> </table> <center> <a href="https://huggingface.co/spaces/Yuliang/ICON?duplicate=true"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-lg-dark.svg"/></a> <h2> The reconstruction + refinement + video takes about 200 seconds for a single image. </h2> <h2><span style="color:red"> ICON is only suitable for humanoid images and will not work well on cartoons with non-human shapes.</span></h2> </center> <details> <summary>More</summary> #### Citation ``` @inproceedings{xiu2022icon, title = {{ICON}: {I}mplicit {C}lothed humans {O}btained from {N}ormals}, author = {Xiu, Yuliang and Yang, Jinlong and Tzionas, Dimitrios and Black, Michael J.}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {13296-13306} } ``` #### Acknowledgments: - [StyleGAN-Human, ECCV 2022](https://stylegan-human.github.io/) - [nagolinc/styleGanHuman_and_PIFu](https://huggingface.co/spaces/nagolinc/styleGanHuman_and_PIFu) - [radames/PIFu-Clothed-Human-Digitization](https://huggingface.co/spaces/radames/PIFu-Clothed-Human-Digitization) #### Image Credits * [Pinterest](https://www.pinterest.com/search/pins/?q=parkour&rs=sitelinks_searchbox) #### Related works * [ICON @ MPI](https://icon.is.tue.mpg.de/) * [MonoPort @ USC](https://xiuyuliang.cn/monoport) * [Phorhum @ Google](https://phorhum.github.io/) * [PIFuHD @ Meta](https://shunsukesaito.github.io/PIFuHD/) * [PaMIR @ Tsinghua](http://www.liuyebin.com/pamir/pamir.html) </details> ''' def generate_image(seed, psi): iface = gr.Interface.load("spaces/hysts/StyleGAN-Human") img = iface(seed, psi) return img model_types = ['ICON', 'PIFu', 'PaMIR'] examples_names = glob.glob('examples/*.png') examples_types = np.random.choice( model_types, len(examples_names), p=[0.6, 0.2, 0.2]) examples = [list(item) for item in zip(examples_names, examples_types)] with gr.Blocks() as demo: gr.Markdown(description) out_lst = [] with gr.Row(): with gr.Column(): with gr.Row(): with gr.Column(): seed = gr.inputs.Slider( 0, 1000, step=1, default=0, label='Seed (For Image Generation)') psi = gr.inputs.Slider( 0, 2, step=0.05, default=0.7, label='Truncation psi (For Image Generation)') radio_choice = gr.Radio( model_types, label='Method (For Reconstruction)', value='icon-filter') inp = gr.Image(type="filepath", label="Input Image") with gr.Row(): btn_sample = gr.Button("Generate Image") btn_submit = gr.Button("Submit Image") gr.Examples(examples=examples, inputs=[inp, radio_choice], cache_examples=False, fn=generate_model, outputs=out_lst) out_vid = gr.Video( label="Image + Normal + SMPL Body + Clothed Human") out_vid_download = gr.File( label="Download Video, welcome share on Twitter with #ICON") with gr.Column(): overlap_inp = gr.Image( type="filepath", label="Image Normal Overlap") out_final = gr.Model3D( clear_color=[0.0, 0.0, 0.0, 0.0], label="Clothed human") out_final_download = gr.File( label="Download clothed human mesh") out_smpl = gr.Model3D( clear_color=[0.0, 0.0, 0.0, 0.0], label="SMPL body") out_smpl_download = gr.File(label="Download SMPL body mesh") out_smpl_npy_download = gr.File(label="Download SMPL params") out_lst = [out_smpl, out_smpl_download, out_smpl_npy_download, out_final, out_final_download, out_vid, out_vid_download, overlap_inp] btn_submit.click(fn=generate_model, inputs=[ inp, radio_choice], outputs=out_lst) btn_sample.click(fn=generate_image, inputs=[seed, psi], outputs=inp) if __name__ == "__main__": # demo.launch(debug=False, enable_queue=False, # auth=(os.environ['USER'], os.environ['PASSWORD']), # auth_message="Register at icon.is.tue.mpg.de to get HuggingFace username and password.") demo.launch(debug=True, enable_queue=True)