CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image

2026-09-01

AI Summary

该研究提出了一种基于四元数约束的连续归一化流方法CQF-HMR，用于从单张图像中恢复3D人体网格，解决了因深度信息丢失导致的问题。

Abstract

Recovering 3D digital humans from a single 2D image is an ill-posed computer vision problem due to the loss of depth information. Probabilistic 3D human pose estimation compensates for this by estimating a set of 3D hypotheses from a prior distribution via generative models. However, most prior work focuses only on 3D keypoints, which often leads to implausible poses that are difficult to apply to downstream tasks, e.g. animation or digital humans. SMPL-based methods are more scalable thanks to the explicit body priors, but it requires more complex modeling of the generation process due to the non-additive nature of the joint rotations. In this work, we propose a novel approach for probabilistic 3D humans using quaternion-constrained continuous normalizing flows conditioned on 2D pose estimations. Our proposed quaternion flows show significant advantages over approaches using other rotation representations. Experiments demonstrate state-of-the-art results of our method on Human3.6M, particularly in ambiguous settings, and comparable pose estimation accuracy on challenging 3DPW and EMDB benchmarks.

Problem

• 3D Human Mesh Recovery
• Single Image
• Probabilistic 3D Pose Estimation
• Ill-posed Problem

Innovation

• Quaternion Flows
• Probabilistic 3D Human Mesh Recovery
• Continuous Normalizing Flows
• 2D Pose Estimations