用概率模型提升3D人体姿态估计的准确性与鲁棒性
ProPLIKS: Probablistic 3D human body pose estimation
- 基于SO(3)旋转群的归一化流，结合莫比乌斯变换建模姿态分布
- 在RGB和医学X-Ray数据集上均优于现有方法，有效处理姿态不确定性
- 适合需要高精度姿态估计的医疗、动作捕捉等场景
我们提出一种新的3D人体姿态估计方法，采用概率建模技术。该方法利用归一化流在非欧几里得几何中的优势，针对姿态不确定性问题进行建模。具体而言，我们设计了适配SO(3)旋转群的归一化流，并引入基于莫比乌斯变换的耦合机制，能够准确表示SO(3)上的任意概率分布，有效解决连续性问题。此外，我们将从2D像素对齐输入重建3D人体的任务重新理解为映射到一组可能姿态的挑战，承认任务固有的模糊性，并实现多视角情形下的简便融合。这些策略的结合展示了概率模型在复杂场景下人体姿态估计中的有效性。我们的方法在姿态估计领域显著超越现有方法，并在RGB图像及医学X-Ray数据集上进行了验证。
原文摘要 · Abstract (English)
We present a novel approach for 3D human pose estimation by employing probabilistic modeling. This approach leverages the advantages of normalizing flows in non-Euclidean geometries to address uncertain poses. Specifically, our method employs normalizing flow tailored to the SO(3) rotational group, incorporating a coupling mechanism based on the Möbius transformation. This enables the framework to accurately represent any distribution on SO(3), effectively addressing issues related to discontinuities. Additionally, we reinterpret the challenge of reconstructing 3D human figures from 2D pixel-aligned inputs as the task of mapping these inputs to a range of probable poses. This perspective acknowledges the intrinsic ambiguity of the task and facilitates a straightforward integration method for multi-view scenarios. The combination of these strategies showcases the effectiveness of probabilistic models in complex scenarios for human pose estimation techniques. Our approach notably surpasses existing methods in the field of pose estimation. We also validate our methodology on human pose estimation from RGB images as well as medical X-Ray datasets.
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