Ray-Flow HMR: Probabilistic Human Mesh Recovery with Accurate, Consistent, and Diverse Samples

Monocular human mesh recovery is an inherently ambiguous task. This motivates a probabilistic approach. Recent works learn the distribution of body meshes conditioned on the image. However, these methods often produce samples that lack accuracy, consistency, diversity, or some combination of these. We argue that learning image-consistent samples in body-model parameter space is difficult, since the mapping that goes through body geometry and perspective projection is complex and non-linear. In this work, we propose to enforce keypoint consistency at model construction, by parameterising 3D keypoints as depths along camera rays formed by 2D keypoints. Our model, Ray-Flow HMR, generates body samples in three stages: first, we sample 2D keypoints using an image-conditioned normalising flow; then, we sample their depths along the corresponding rays using another conditional normalising flow; finally, we regress body meshes via a learned inverse kinematics module. This construction ensures that sampled 3D keypoints project to the conditioning 2D keypoints, allowing for inverse kinematics to better preserve image-space consistency for the mesh samples. On in-the-wild datasets, Ray-Flow HMR outperforms probabilistic baselines in accuracy, consistency, and diversity, while remaining competitive with deterministic methods.