FMPose3D: monocular 3D pose estimation via flow matching

Monocular 3D pose estimation is fundamentally ill-posed due to depth ambiguity and occlusions, thereby motivating probabilistic methods that generate multiple plausible 3D pose hypotheses.In particular, diffusion-based models have demonstrated strong performance, but their iterative denoising process typically requires many time steps for each prediction, making inference computationally expensive.In contrast, Flow Matching (FM) learns an ODE-based velocity field, enabling efficient generation of 3D pose samples with only a few integration steps. Inspired by this capability, we propose a novel generative pose estimation framework, FMPose, that formulates 3D pose estimation as a conditional distribution transport problem. It continuously transports samples from a standard Gaussian prior to the distribution of plausible 3D poses conditioned on 2D inputs. While the ODE trajectories are deterministic, FMPose naturally generates diverse pose hypotheses by sampling different noise seeds.To obtain a single accurate prediction from those hypotheses, we further introduce a Reprojection-based Posterior Expectation Aggregation (RPEA) module, which approximates the Bayesian posterior expectation over 3D hypotheses. FMPose surpasses existing methods on the widely used human pose estimation benchmarks Human3.6M and MPI-INF-3DHP, and further achieves state-of-the-art performance on the 3D animal pose datasets Animal3D and CtrlAni3D, demonstrating strong performance across both human and animal 3D pose domains.