We address the problem of fitting a parametric human body model (SMPL) to
point cloud data. Optimization-based methods require careful initialization and
are prone to becoming trapped in local optima. Learning-based methods address
this but do not generalize well when the input pose is far from those seen
during training. For rigid point clouds, remarkable generalization has been
achieved by leveraging SE(3)-equivariant networks, but these methods do not
work on articulated objects. In this work we extend this idea to human bodies
and propose ArtEq, a novel part-based SE(3)-equivariant neural architecture for
SMPL model estimation from point clouds. Specifically, we learn a part
detection network by leveraging local SO(3) invariance, and regress shape and
pose using articulated SE(3) shape-invariant and pose-equivariant networks, all
trained end-to-end. Our novel equivariant pose regression module leverages the
permutation-equivariant property of self-attention layers to preserve
rotational equivariance. Experimental results show that ArtEq can generalize to
poses not seen during training, outperforming state-of-the-art methods by
74.5%, without requiring an optimization refinement step. Further, compared
with competing works, our method is more than three orders of magnitude faster
during inference and has 97.3% fewer parameters. The code and model will be
available for research purposes at this https URL

本文介绍了一种将参数化人体模型适应于点云数据的新方法 ——ArtEq。通过使用基于部分的 SE（3）- 等变神经结构来完成 SMPL 模型对点云的估计，并通过对等变姿态回归模块进行改进来实现对训练过程之外的姿态的泛化能力的提升。在实验中，ArtEq 方法的表现比同类方法提高了 74.5%，并且速度快了三个数量级。