{"id":20857800,"url":"https://github.com/zju3dv/instant-nvr","last_synced_at":"2025-06-24T13:31:46.600Z","repository":{"id":174974215,"uuid":"652694165","full_name":"zju3dv/instant-nvr","owner":"zju3dv","description":"[CVPR 2023] Code for \"Learning Neural Volumetric Representations of Dynamic Humans in 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Learning Neural Volumetric Representations of Dynamic Humans in Minutes\n\n### [Project Page](https://zju3dv.github.io/instant_nvr) | [Video](https://zju3dv.github.io/instant_nvr) | [Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Geng_Learning_Neural_Volumetric_Representations_of_Dynamic_Humans_in_Minutes_CVPR_2023_paper.pdf) | [Data](https://github.com/zju3dv/instant-nvr/blob/master/docs/install.md#set-up-datasets)\n\n![inb](docs/media/inb.gif)\n\n\u003e [Learning Neural Volumetric Representations of Dynamic Humans in Minutes](https://zju3dv.github.io/instant_nvr)\n\u003e\n\u003e Chen Geng\\*, Sida Peng\\*, Zhen Xu\\*, Hujun Bao, Xiaowei Zhou (* denotes equal contribution)\n\u003e\n\u003e CVPR 2023\n\n## Installation\n\nSee [here](./docs/install.md).\n\n## Reproducing results in the paper\n\nWe provide two scripts to help reproduce the results shown in the paper.\n\nAfter installing the environment and the dataset, for evaluation on the ZJU-MoCap dataset, run:\n\n```shell\nsh scripts/eval_zjumocap.sh\n```\n\nFor evaluation on the MonoCap dataset, run:\n\n```shell\nsh scripts/eval_monocap.sh\n```\n\n\n## Evaluation on ZJU-MoCap\n\nLet's take \"377\" as an example.\n\nTraining on ZJU-MoCap can be done by running.\n\n```shell\nexport name=377\npython train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS} \n```\n\nEvaluation can be done by running:\n```shell\nexport name=377\npython run.py --type evaluate --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}\n```\n\n## Evaluation on MonoCap\n\nLet's take \"lan\" as an example.\n\nTraining on Monocap can be done by running: \n\n```shell\nexport name=lan\npython train_net.py --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS} \n```\n\nEvaluation can be done by running:\n```shell\nexport name=lan\npython run.py --type evaluate --cfg_file configs/inb/inb_${name}.yaml exp_name inb_${name} gpus ${GPUS}\n```\n\n## TODO List\n\nThis repository currently serves as the release of the technical paper's implementation and will undergo future updates (planned below) to enhance user-friendliness. We warmly welcome and appreciate any contributions.\n\n- [x] Instruction on running on custom datasets (Kudos to [@tian42chen](https://github.com/tian42chen)!!)\n- [ ] Add support for further acceleration using CUDA\n- [ ] Add a Google Colab notebook demo\n\n## Bibtex\n\nIf you find the repo useful for your research, please consider citing our paper:\n\n```\n@inproceedings{instant_nvr,\n    title={Learning Neural Volumetric Representations of Dynamic Humans in Minutes},\n    author={Chen Geng and Sida Peng and Zhen Xu and Hujun Bao and Xiaowei Zhou},\n    booktitle={CVPR},\n    year={2023}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzju3dv%2Finstant-nvr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzju3dv%2Finstant-nvr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzju3dv%2Finstant-nvr/lists"}