Multiple View Geometry Transformers for 3D Human Pose Estimation
Ziwei Liao Jialiang Zhu Chunyu Wang Han Hu Steven L. Waslander
In this work, we aim to improve the 3D reasoning ability of Transformers in
multi-view 3D human pose estimation. Recent works have focused on end-to-end
learning-based transformer designs, which struggle to resolve geometric
information accurately, particularly during occlusion. Instead, we propose a
novel hybrid model, MVGFormer, which has a series of geometric and appearance
modules organized in an iterative manner. The geometry modules are
learning-free and handle all viewpoint-dependent 3D tasks geometrically which
notably improves the model's generalization ability. The appearance modules are
learnable and are dedicated to estimating 2D poses from image signals
end-to-end which enables them to achieve accurate estimates even when occlusion
occurs, leading to a model that is both accurate and generalizable to new
cameras and geometries. We evaluate our approach for both in-domain and
out-of-domain settings, where our model consistently outperforms
state-of-the-art methods, and especially does so by a significant margin in the
out-of-domain setting. We will release the code and models:
https://github.com/XunshanMan/MVGFormer.