Awesome
Genebody-Benchmarks
This repository contains the training and evaluation code for NeuralBody, NeuralVolumes, NeuralHumanRendering, NeuralTexture, A-NeRF and IBRNet to perform novel-view synthesis on Genebody dataset. Following benchmark tables are also shown in the paper.
The code for each method is on the branches of this repository. To re-implement the results on GeneBody, please download the pretrained models in the Model Zoo first, and prepare the environment and dataset based on the README.md
on each branch.
News
[29/04/22]: First version of benchmarks released, containing 5 case-specific methods and 1 generalizable methods.
Benchmarks
Case-specific Methods on Genebody
Model | PSNR | SSIM | LPIPS | ckpts |
---|---|---|---|---|
NV | 19.86 | 0.774 | 0.267 | ckpts |
NHR | 20.05 | 0.800 | 0.155 | ckpts |
NT | 21.68 | 0.881 | 0.152 | ckpts |
NB | 20.73 | 0.878 | 0.231 | ckpts |
A-Nerf | 15.57 | 0.508 | 0.242 | ckpts |
(see detail why A-Nerf's performance is counterproductive in issue)
Generalizable Methods on Genebody
Model | PSNR | SSIM | LPIPS | ckpts |
---|---|---|---|---|
PixelNeRF (Our implemetation coming soon) | 24.15 | 0.903 | 0.122 | |
IBRNet | 23.61 | 0.836 | 0.177 | ckpts |
Citation
@article{cheng2022generalizable,
title={Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis},
author={Cheng, Wei and Xu, Su and Piao, Jingtan and Qian, Chen and Wu, Wayne and Lin, Kwan-Yee and Li, Hongsheng},
journal={arXiv preprint arXiv:2204.11798},
year={2022}
}