We present a novel pipeline for learning high-quality triangular human avatars from multi-view videos. Recent methods for avatar learning are typically based on neural radiance fields (NeRF), which is not compatible with traditional graphics pipeline and poses great challenges for operations like editing or synthesizing under different environments. To overcome these limitations, our method represents the avatar with an explicit triangular mesh extracted from an implicit SDF field, complemented by an implicit material field conditioned on given poses. Leveraging this triangular avatar representation, we incorporate physics-based rendering to accurately decompose geometry and texture. To enhance both the geometric and appearance details, we further employ a 2D UNet as the network backbone and introduce pseudo normal ground-truth as additional supervision. Experiments show that our method can learn triangular avatars with high-quality geometry reconstruction and plausible material decomposition, inherently supporting editing, manipulation or relighting operations.
Ours | AvatarReX | Animatable Gaussians |
Animatable Gaussians* |
Xu et al. | Lin et al. | Intrinsic Avatar |
|
---|---|---|---|---|---|---|---|
Representation | mesh | SDF | 3DGS | 3DGS | SDF | SDF | SDF |
Relightable? | ✔ | ✔ | ✔ | ✔ | ✔ | ||
Training Time (~100 frames) |
~3h | 2.5 days | 4h (mono.) |
||||
Training Time (~1000 frames) |
~16h | 2 days | 2 days (RTX 4090) |
2 days (RTX 4090) |
30h | ||
Inference Time (per image) |
180ms | 30s | 100ms | 4~10s | 5s | 40s | 20s |
@misc{chen2024meshavatar,
title={MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos},
author={Yushuo Chen and Zerong Zheng and Zhe Li and Chao Xu and Yebin Liu},
year={2024},
eprint={2407.08414},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.08414},
}