用流匹配方法提升单目3D人体姿态估计的准确性与速度
Flow Matching for Probabilistic Monocular 3D Human Pose Estimation
- 基于流匹配生成模型，从2D关键点推断3D姿态分布
- 在Human3.6M和MPI-INF-3DHP上优于现有方法，速度更快
- 适合需要高精度与实时性的3D姿态估计应用场景
从单目摄像头恢复3D人体姿态因深度模糊而极具挑战。以往的2D到3D姿态提升方法常产生错误却过度自信的结果。为缓解此问题，新兴的概率方法将3D估计视为分布，考虑姿态不确定性。本文提出FMPose，一种基于流匹配生成框架的3D人体姿态估计方法。该方法在2D线索条件下，通过连续归一化流学习从简单源分布到合理3D姿态分布的最优传输路径。2D特征提取采用图卷积网络，利用人体关节间的可学习连接作为图结构进行特征聚合。在精度相当的情况下，FMPose处理速度显著快于扩散模型，并提供另一更快速且更准确的配置。实验表明，FMPose在两个主流3D人体姿态估计基准（Human3.6M、MPI-INF-3DHP）上均取得显著提升，在更具挑战性的3DPW数据集上也表现优异。代码已公开于https://github.com/cuongle1206/FMPose。
原文摘要 · Abstract (English)
Recovering 3D human poses from a monocular camera view is a highly ill-posed problem due to the depth ambiguity. Earlier studies on 3D human pose lifting from 2D often contain incorrect-yet-overconfident 3D estimations. To mitigate the problem, emerging probabilistic approaches treat the 3D estimations as a distribution, taking into account the uncertainty measurement of the poses. Falling in a similar category, we proposed FMPose, a probabilistic 3D human pose estimation method based on the flow matching generative approach. Conditioned on the 2D cues, the flow matching scheme learns the optimal transport from a simple source distribution to the plausible 3D human pose distribution via continuous normalizing flows. The 2D lifting condition is modeled via graph convolutional networks, leveraging the learnable connections between human body joints as the graph structure for feature aggregation. While trade-offs between processing time and precision exist, already in the equal-accuracy comparison, FMPose exhibits significantly faster processing time than the diffusion model, and also offers another faster and more accurate configuration. Experimental results show major improvements of our FMPose over current state-of-the-art methods on two common benchmarks for 3D human pose estimation, namely Human3.6M, MPI-INF-3DHP. Additionally, FMPose shows competitive performance on the more challenging 3DPW dataset. The code implementation is available at https://github.com/cuongle1206/FMPose
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