HMNeRFBench: A Comprehensive Benchmark for Neural Human Radiance Fields

Kenkun Liu1,2, Derong Jin2, Ailing Zeng†1, Xiaoguang Han2, Lei Zhang1
1International Digital Economy Academy (IDEA), 2Chinese University of Hong Kong (Shenzhen)
NeurIPS 2023
†Corresponding Author
Paper Supplementary Code arXiv

Qualitative comparison of representative human-centric NeRF methods on challenging datasets

Abstract

The past two years have witnessed a significant increase in interest concerning NeRF-based human body rendering. While this surge has propelled considerable advancements,particularly in terms of dense-view inputs to monocular videos and generalizable and animatable capabilities, it has also led to an influx of methods and datasets. This explosion complicates experimental settings and makes fair comparisons challenging. In this work, we design and execute thorough studies into unified evaluation settings and metrics to establish a fair and reasonable benchmark for human NeRF models. To reveal the effects of extant models, we benchmark them against diverse and hard scenes. Additionally, we construct a cross-subject benchmark pre-trained on large-scale datasets to assess generalizable methods. Finally, we analyze the essential components for animatability and generalizability, and make HumanNeRF from monocular videos generalizable, as the inaugural baseline. We hope these benchmarks and analyses could serve the community.

Unifying Settings

Benchmarking Scene-specific Methods

Benchmarking Generalizable Methods

GeneHumanNeRF: Animatable and Generalizable Human NeRF from Monocular Videos

Datasets

HuMMan
GeneBody
ZJU-MoCap

BibTeX

@inproceedings{liu2023comprehensive,
        title={A Comprehensive Benchmark for Neural Human Radiance Fields},
        author={Liu, Kenkun and Jin, Derong and Zeng, Ailing and Han, Xiaoguang and Zhang, Lei},
        booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
        year={2023}
      }