Awesome

News :new:

• [2022/10/01] Combine ICON with our relative spatial keypoint encoding for fast and convenient monocular reconstruction, without requiring the expensive SMPL feature. More details are here.

Installation

Please install python dependencies specified in environment.yml:

conda env create -f environment.yml
conda activate KeypointNeRF

Data preparation

Please see DATA_PREP.md to setup the ZJU-MoCap dataset.

After this step the data directory follows the structure:

./data/zju_mocap
├── CoreView_313
├── CoreView_315
├── CoreView_377
├── CoreView_386
├── CoreView_387
├── CoreView_390
├── CoreView_392
├── CoreView_393
├── CoreView_394
└── CoreView_396

Train your own model on the ZJU dataset

Execute train.py script to train the model on the ZJU dataset.

python train.py --config ./configs/zju.json --data_root ./data/zju_mocap

After the training, the model checkpoint will be stored under ./EXPERIMENTS/zju/ckpts/last.ckpt, which is equivalent to the one provided here.

Evaluation

To extract render and evaluate images, execute:

python train.py --config ./configs/zju.json --data_root ./data/zju_mocap --run_val
python eval_zju.py --src_dir ./EXPERIMENTS/zju/images_v3

To visualize the dynamic results, execute:

python render_dynamic.py --config ./configs/zju.json --data_root ./data/zju_mocap --model_ckpt ./EXPERIMENTS/zju/ckpts/last.ckpt

We compare KeypointNeRF with recent state-of-the-art methods. The evaluation metric is SSIM and PSNR.

Reconstruction from a Single Image

Our relative spatial encoding can be used to reconstruct humans from a single image. As a example, we leverage ICON and replace its expensive SDF feature with our relative spatial encoding.

3D Human Reconstruction on CAPE

Check the benchmark here and more details here.

Publication

If you find our code or paper useful, please consider citing:

@inproceedings{Mihajlovic:ECCV2022,
  title = {{KeypointNeRF}: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints},
  author = {Mihajlovic, Marko and Bansal, Aayush and Zollhoefer, Michael and Tang, Siyu and Saito, Shunsuke},
  booktitle={European conference on computer vision},
  year={2022},
}

License

CC-BY-NC 4.0. See the LICENSE file.