  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 pose regression module leverages the
permutation-equivariant property of self-attention layers to preserve
rotational equivariance. Experimental results show that ArtEq generalizes to
poses not seen during training, outperforming state-of-the-art methods by ~44%
in terms of body reconstruction accuracy, without requiring an optimization
refinement step. Furthermore, ArtEq is three orders of magnitude faster during
inference than prior work and has 97.3% fewer parameters. The code and model
are available for research purposes at https://arteq.is.tue.mpg.de.
