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| import os | |
| import shutil | |
| import subprocess | |
| import textwrap | |
| from pathlib import Path | |
| import gradio as gr | |
| import torch | |
| from huggingface_hub import hf_hub_download | |
| REPO_ID = "kbrodt/sketch2pose" | |
| API_TOKEN = os.environ["sketch2pose"] | |
| ASSET_DIR = Path("./assets") | |
| SAVE_DIR = "output" | |
| CMD = textwrap.dedent(""" | |
| python src/pose.py | |
| --save-path {} | |
| --img-path {} | |
| """) | |
| TITLE = "Sketch2Pose: Estimating a 3D Character Pose from a Bitmap Sketch" | |
| DESCRIPTION = ''' | |
| <head> | |
| <style> | |
| html { | |
| margin: 0; | |
| padding: 0; | |
| } | |
| body { | |
| margin: 0 auto; | |
| padding: 0.5em; | |
| text-align: justify; | |
| max-width: 800px; | |
| } | |
| figure { | |
| margin: 0; | |
| font-size: smaller; | |
| text-align: justify; | |
| } | |
| img { | |
| width: auto; | |
| max-width: 100%; | |
| height: auto; | |
| } | |
| video { | |
| width: auto; | |
| max-width: 100%; | |
| height: auto; | |
| } | |
| ul.horizontal { | |
| padding: 0; | |
| } | |
| ul.horizontal li { | |
| padding: 0 1em 0 0; | |
| display: inline-block; | |
| } | |
| </style> | |
| </head> | |
| <figure> | |
| <img src="http://www-labs.iro.umontreal.ca/~bmpix/sketch2pose/teaser.png" alt="sketch2pose"> | |
| <figcaption> | |
| Given a single natural <b>bitmap</b> sketch of a character (a), our | |
| learning-based approach allows to automatically, with no additional input, | |
| recover the 3D pose consistent with the viewer expectation (b). This pose can | |
| be then automatically copied a custom rigged and skinned 3D character (c) using | |
| standard retargeting tools (d). Input image © Olga Posukh. | |
| </figcaption> | |
| </figure> | |
| <p> | |
| <ul class="horizontal"> | |
| <li><a href="https://dl.acm.org/doi/10.1145/3528223.3530106">[paper acm siggraph 2022]</a></li> | |
| <li><a href="https://github.com/kbrodt/sketch2pose">[code.git]</a> | |
| <iframe src="https://ghbtns.com/github-btn.html?user=kbrodt&repo=sketch2pose&type=star&count=true&v=2&size=small" frameborder="0" scrolling="0" width="100" height="20"></iframe> | |
| </li> | |
| </ul> | |
| </p> | |
| <video width="560" height="315" controls autoplay muted loop> | |
| <source src="http://www-labs.iro.umontreal.ca/~bmpix/sketch2pose/sketch2pose.webm" type="video/mp4"> | |
| </video> | |
| ''' | |
| def prepare(): | |
| filename = "models_smplx_v1_1.zip" | |
| smpl_path = hf_hub_download( | |
| repo_id=REPO_ID, | |
| repo_type="model", | |
| filename=filename, | |
| use_auth_token=API_TOKEN, | |
| cache_dir=ASSET_DIR, | |
| ) | |
| if not (ASSET_DIR / filename).is_file(): | |
| shutil.copy(smpl_path, ASSET_DIR) | |
| subprocess.run("bash ./scripts/download.sh".split()) | |
| subprocess.run("bash ./scripts/prepare.sh".split()) | |
| def main(): | |
| prepare() | |
| save_dir = Path(SAVE_DIR) | |
| save_dir.mkdir(parents=True, exist_ok=True) | |
| def pose(img_path, use_cos=True, use_angle_transf=True, use_contacts=False, use_natural=True): | |
| if use_cos == False: | |
| use_angle_transf = False | |
| cmd = CMD.format(save_dir, img_path) | |
| if use_cos: | |
| cmd = cmd + " --use-cos" | |
| if use_angle_transf: | |
| cmd = cmd + " --use-angle-transf" | |
| if use_contacts: | |
| cmd = cmd + " --use-contacts" | |
| if use_natural: | |
| cmd = cmd + " --use-natural" | |
| out_dir = (save_dir / Path(img_path).name).with_suffix("") | |
| mesh_path = out_dir / "us.glb" | |
| if not mesh_path.is_file(): | |
| subprocess.call(cmd.split()) | |
| return str(mesh_path) | |
| examples = [] | |
| for img_path in Path("./data/images").glob("*"): | |
| examples.append([str(img_path), True, True, False, True]) | |
| break | |
| demo = gr.Interface( | |
| fn=pose, | |
| inputs=[ | |
| gr.Image(type="filepath", label="Image"), | |
| gr.Checkbox(value=True, label="Bone lenghts"), | |
| gr.Checkbox(value=True, label="Foreshortening"), | |
| gr.Checkbox(value=False, label="Self-contacts (available with cuda)", interactive=torch.cuda.is_available()), | |
| gr.Checkbox(value=True, label="Pose naturalness"), | |
| ], | |
| outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="SMPL 3D pose"), | |
| examples=examples, | |
| title=TITLE, | |
| description=DESCRIPTION, | |
| ) | |
| demo.launch() | |
| if __name__ == "__main__": | |
| main() | |