Awesome

Lab4D

[Docs & Tutorials] [Data & Checkpoints]

This is an alpha release and the APIs are subject to change. Please provide feedback and report bugs via github issues. Thank you for your support.

About

Lab4D is a framework for 4D reconstruction from monocular videos. The software is licensed under the MIT license.

TODOs

• web viewer (see PPR branch)
• evaluation (see PPR branch) and benchmarks
• multi-view reconstruction
• feedforward models (see DASR)

Acknowledgement

• Our pre-processing pipeline is built upon the following open-sourced repos:
  • Segmentation: Track-Anything, Grounding-DINO
  • Feature & correspondence: DensePose-CSE, DINOv2, VCNPlus
  • Depth: ZoeDepth
  • Camera: BANMo-viewpoint
• We use dqtorch for efficient rotation operations
• We thank @mjlbach, @alexanderbergman7, and @terrancewang for testing and feedback
• We thank @jasonyzhang, @MightyChaos, @JudyYe, and @andrewsonga for feedback

If you use this project for your research, please consider citing the following papers.

For building deformable object models, cite:

@inproceedings{yang2022banmo,
  title={BANMo: Building Animatable 3D Neural Models from Many Casual Videos},
  author={Yang, Gengshan and Vo, Minh and Neverova, Natalia and Ramanan, Deva and Vedaldi, Andrea and Joo, Hanbyul},
  booktitle = {CVPR},
  year={2022}
}  

For building category body and pose models, cite:

@inproceedings{yang2023rac,
    title={Reconstructing Animatable Categories from Videos},
    author={Yang, Gengshan and Wang, Chaoyang and Reddy, N. Dinesh and Ramanan, Deva},
    booktitle = {CVPR},
    year={2023}
} 

For object-scene reconstruction and extreme view synthesis, cite:

@article{song2023totalrecon,
  title={Total-Recon: Deformable Scene Reconstruction for Embodied View Synthesis},
  author={Song, Chonghyuk and Yang, Gengshan and Deng, Kangle and Zhu, Jun-Yan and Ramanan, Deva},
  journal={arXiv},
  year={2023}
}

For training feed-forward video/image shape and pose estimators, cite:

@inproceedings{tan2023distilling,
  title={Distilling Neural Fields for Real-Time Articulated Shape Reconstruction},
  author={Tan, Jeff and Yang, Gengshan and Ramanan, Deva},
  booktitle={CVPR},
  year={2023}
}

For the human-48 dataset cite:

@incollection{vlasic2008articulated,
  title={Articulated mesh animation from multi-view silhouettes},
  author={Vlasic, Daniel and Baran, Ilya and Matusik, Wojciech and Popovi{\'c}, Jovan},
  booktitle={Acm Siggraph 2008 papers},
  pages={1--9},
  year={2008}
}
@article{xu2018monoperfcap,
  title={Monoperfcap: Human performance capture from monocular video},
  author={Xu, Weipeng and Chatterjee, Avishek and Zollh{\"o}fer, Michael and Rhodin, Helge and Mehta, Dushyant and Seidel, Hans-Peter and Theobalt, Christian},
  journal={ACM Transactions on Graphics (ToG)},
  volume={37},
  number={2},
  pages={1--15},
  year={2018},
  publisher={ACM New York, NY, USA}
}
@inproceedings{perazzi2016benchmark,
  title={A benchmark dataset and evaluation methodology for video object segmentation},
  author={Perazzi, Federico and Pont-Tuset, Jordi and McWilliams, Brian and Van Gool, Luc and Gross, Markus and Sorkine-Hornung, Alexander},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={724--732},
  year={2016}
}