lab4d

A framework for 4D reconstruction from monocular videos.

https://github.com/lab4d-org/lab4d

Science Score: 44.0%

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  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
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  • .zenodo.json file
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  • Scientific vocabulary similarity
    Low similarity (11.5%) to scientific vocabulary

Keywords

4d-reconstruction differentiable-rendering video-reconstruction
Last synced: 10 months ago · JSON representation ·

Repository

A framework for 4D reconstruction from monocular videos.

Basic Info
Statistics
  • Stars: 305
  • Watchers: 15
  • Forks: 20
  • Open Issues: 6
  • Releases: 0
Topics
4d-reconstruction differentiable-rendering video-reconstruction
Created about 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

logo

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.

logo

TODOs

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

Acknowledgement

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} } ```

Owner

  • Name: lab4d-org
  • Login: lab4d-org
  • Kind: organization

Citation (CITATION.cff)

cff-version: 1.2.0
title: Lab4d - A framework for in-the-wild 4D reconstruction from monocular videos
message: 'If you use this software, please cite it as below.'
type: software
authors:
  - family-names: Yang
    given-names: Gengshan
  - family-names: Tan
    given-names: Jeff
  - family-names: Lyons
    given-names: Alex
  - family-names: Peri
    given-names: Neehar
  - family-names: Ramanan
    given-names: Deva
url: 'https://github.com/lab4d-org/lab4d'
license: MIT
version: 0.0.0
date-released: '2023-06-30'

GitHub Events

Total
  • Issues event: 3
  • Watch event: 57
  • Member event: 1
  • Issue comment event: 1
  • Push event: 3
  • Pull request review event: 2
  • Pull request event: 2
  • Fork event: 4
Last Year
  • Issues event: 3
  • Watch event: 57
  • Member event: 1
  • Issue comment event: 1
  • Push event: 3
  • Pull request review event: 2
  • Pull request event: 2
  • Fork event: 4

Issues and Pull Requests

Last synced: 10 months ago

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  • Total pull requests: 1
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  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
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  • Bot pull requests: 0
Past Year
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  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
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  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Dependencies

.github/workflows/static.yml actions
  • actions/checkout v3 composite
  • actions/configure-pages v3 composite
  • actions/deploy-pages v2 composite
  • actions/upload-pages-artifact v1 composite
  • conda-incubator/setup-miniconda v2 composite
environment.yml pypi
  • gradio ==3.28.3
  • openmim *
  • pyrender *
  • pysdf *
  • timm ==0.6.7
lab4d/third_party/quaternion/setup.py pypi
preprocess/third_party/vcnplus/models/networks/DCNv2/setup.py pypi
setup.py pypi