NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses
Jing Wen, Alex Schwing, Shenlong Wang
Abstract
We tackle the task of recovering an animatable 3D human avatar from a single or a sparse set of images. For this task, beyond a set of images, many prior state-of-the-art methods use accurate “ground-truth” camera poses and human poses as input to guide reconstruction at test-time. We show that pose‑dependent reconstruction degrades results significantly if pose estimates are noisy. To overcome this, we introduce NoPo-Avatar, which reconstructs avatars solely from images, without any pose input. By removing the dependence of test-time reconstruction on human poses, NoPo-Avatar is not affected by noisy human pose estimates, making it more widely applicable. Experiments on challenging THuman2.0, XHuman, and HuGe100K data show that NoPo-Avatar outperforms existing baselines in practical settings (without ground‑truth poses) and delivers comparable results in lab settings (with ground‑truth poses).
BibTeX
@inproceedings{
wen2025nopoavatar,
title={NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses},
author={Jing Wen and Alex Schwing and Shenlong Wang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=N2QBQh6HeN}
}