  We propose an end-to-end unified 3D mesh recovery of humans and quadruped
animals trained in a weakly-supervised way. Unlike recent work focusing on a
single target class only, we aim to recover 3D mesh of broader classes with a
single multi-task model. However, there exists no dataset that can directly
enable multi-task learning due to the absence of both human and animal
annotations for a single object, e.g., a human image does not have animal pose
annotations; thus, we have to devise a new way to exploit heterogeneous
datasets. To make the unstable disjoint multi-task learning jointly trainable,
we propose to exploit the morphological similarity between humans and animals,
motivated by animal exercise where humans imitate animal poses. We realize the
morphological similarity by semantic correspondences, called sub-keypoint,
which enables joint training of human and animal mesh regression branches.
Besides, we propose class-sensitive regularization methods to avoid a
mean-shape bias and to improve the distinctiveness across multi-classes. Our
method performs favorably against recent uni-modal models on various human and
animal datasets while being far more compact.
