| |
|
|
|
|
| import glob |
| import gradio as gr |
| import numpy as np |
| import os |
| import subprocess |
|
|
| from apps.infer import generate_model |
|
|
| |
|
|
| 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'] |
| 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=True, enable_queue=True) |
|
|
