Pytorch implementation of NHR.

Multi-view Neural Human Rendering
Minye Wu, Yuehao Wang, Qiang Hu, Jingyi Yu.
In CVPR 2020.

We present an end-to-end Neural Human Renderer (NHR) for dynamic human captures under the multi-view setting. NHR adopts PointNet++ for feature extraction (FE) to enable robust 3D correspondence matching on low quality, dynamic 3D reconstructions. To render new views, we map 3D features onto the target camera as a 2D feature map and employ an anti-aliased CNN to handle holes and noises. Newly synthesized views from NHR can be further used to construct visual hulls to handle textureless and/or dark regions such as black clothing. Comprehensive experiments show NHR significantly outperforms the state-of-the-art neural and image-based rendering techniques, especially on hands, hair, nose, foot, etc.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

All material is made available under Creative Commons BY-NC-SA 4.0 license by Adobe Inc. You can use, redistribute, and adapt the material for non-commercial purposes, as long as you give appropriate credit by citing our paper and indicating any changes that you've made.

The designed architecture follows this guide PyTorch-Project-Template, you can check each folder's purpose by yourself.

Dependencies:

• python 3.6
• pytorch>=1.2
• torchvision
• opencv
• ignite=0.2.0
• yacs
• Antialiased CNNs
• Pointnet2.PyTorch
• apex
• PCPR

1.Dataset preparing A data folder with a structure like following:

.
├──  img
│    └── %d    					- the frame number, start from 0.
│        └──mask
│        	└── img_%04d.jpg	- foreground mask of corresponding view. view number start from 0. 
│        └──img_%04d.jpg   		- undistorted RGB images for each view. view number start from 0.
│
├──  pointclouds				
│    └── frame%d.npy			- point cloud for each frame. A numpy array with a size of Nx6, where N is the size of point cloud. Each row is the "x y z r g b". The frame number start from 1.
│
├──  CamPose.inf				-Camera extrinsics. In each row, the 3x4 [R T] matrix is displayed in columns, with the third column followed by columns 1, 2, and 4, where R*X^{camera}+T=X^{world}.
│
└──  Intrinsic.inf				-Camera intrinsics. The format of each intrinsics is: "idx \n fx 0 cx \n 0 fy cy \n 0 0 1 \n \n" (idx starts from 0)

2. Network Training

• modify configure file config.yml
• start training cd tools && python train_net.py <gpu id>

2. Network Fine-tuning

• run cd tools && python finetune.py <gpu id> <path to checkpoint> <the number of resuming epoch>

3. Rendering

• Coming soon