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Representing Volumetric Videos as Dynamic MLP Maps

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Representing Volumetric Videos as Dynamic MLP Maps
Sida Peng*, Yunzhi Yan*, Qing Shuai, Hujun Bao, Xiaowei Zhou (* equal contribution)
CVPR 2023

Any questions or discussions are welcomed!

Installation

Please see INSTALL.md for manual installation.

Interactive demo

Please see INSTALL.md to download the dataset. We provide the pretrained models at here.

Take the rendering on sequence 313 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/zjumocap/313/final.pth.

2. Interactive rendering demo:

   python gui.py --config configs/zjumocap/dymap_313.py fast_render True 
   

Please see INSTALL.md to download the dataset. We provide the pretrained models at here.

Take the rendering on sequence sport1 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/nhr/sport1/final.pth.

2. Interactive rendering demo:

   python gui.py --config configs/nhr/sport1.py fast_render True 
   

Run the code on ZJU-MoCap

Please see INSTALL.md to download the dataset.

We provide the pretrained models at here.

Take the test on sequence 313 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/zjumocap/313/final.pth.

2. Test on unseen views:

   python run.py --config configs/zjumocap/dymap_313.py mode evaluate fast_render True
   

Take the visualization on sequence 313 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/zjumocap/313.

2. Visualization:

   • Visualize free-viewpoint videos

   python run.py --config configs/zjumocap/dymap_313.py mode visualize vis_novel_view True fast_render True
   

   

   • Visualize novel views of single frame

   python run.py --config configs/zjumocap/dymap_313.py mode visualize vis_novel_view True fixed_time True fast_render True
   

   

   • Visualize the dynamic scene with fixed camera

   python run.py --config configs/zjumocap/dymap_313.py mode visualize vis_novel_view True fixed_view True fast_render True
   

   

   • Visualize mesh

   python run.py --config configs/zjumocap/dymap_313.py  mode visualize vis_mesh True  fast_render True    
   

Take the training on sequence 313 as an example.

1. Train:

   # training
   python train_net.py --config configs/zjumocap/dymap_313.py
   # distributed training
   python -m torch.distributed.launch --nproc_per_node=4 train_net.py --config configs/zjumocap/dymap_313.py
   

2. Post-process the trained model:

   python run.py --config configs/zjumocap/dymap_313.py mode visualize occ_grid True
   

3. Tensorboard:

   tensorboard --logdir data/record/zjumocap
   

Run the code on NHR

Please see INSTALL.md to download the dataset.

We provide the pretrained models at here.

Take the test on sequence sport1 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/nhr/sport1/final.pth.

2. Test on unseen views:

   python run.py --config configs/nhr/sport1.py mode evaluate fast_render True
   

Take the visualization on sequence sport1 as an example.

1. Download the corresponding pretrained model and put it to $ROOT/data/trained_model/nhr/sport1.

2. Visualization:

   • Visualize novel views

   python run.py --config configs/nhr/sport1.py mode visualize vis_novel_view True fast_render True
   

   

   • Visualize novel views of single frame

   python run.py --config configs/nhr/sport1.py mode visualize vis_novel_view True fixed_time True fast_render True
   

   

   • Visualize the dynamic scene with fixed camera

   python run.py --config configs/nhr/sport1.py mode visualize vis_novel_view True fixed_view True fast_render True
   

   

   • Visualize mesh

   python run.py --config configs/nhr/sport1.py mode visualize vis_mesh True  fast_render True    
   

Take the training on sequence sport1 as an example.

1. Train:

   # training
   python train_net.py --config configs/nhr/sport1.py
   # distributed training
   python -m torch.distributed.launch --nproc_per_node=4 train_net.py --config configs/nhr/sport1.py
   

2. Post-process the trained model:

   python run.py --config configs/nhr/sport1.py mode visualize occ_grid True
   

3. Tensorboard:

   tensorboard --logdir data/record/nhr
   

Citation

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{peng2023representing,
  title={Representing Volumetric Videos as Dynamic MLP Maps},
  author={Peng, Sida and Yan, Yunzhi and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2023}
}