  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.
