论文标题
差异：迈向更可靠的3D姿势估计
DiffPose: Toward More Reliable 3D Pose Estimation
论文作者
论文摘要
由于固有的歧义和阻塞，单程3D人类的姿势估计非常具有挑战性，这通常会导致高度不确定性和不确定性。另一方面，扩散模型最近成为从噪声中产生高质量图像的有效工具。受其能力的启发，我们探索了一个新颖的姿势估计框架（扩散），该框架将3D姿势估计作为反向扩散过程。我们将新颖的设计纳入我们的扩散中，以促进3D姿势估计的扩散过程：姿势不确定性分布的姿势特异性初始化，基于高斯混合模型的正向前向扩散过程以及上下文调节的反向扩散过程。我们提出的差异在广泛使用的姿势估计基准Human36M和MPI-INF-3DHP上明显胜过现有方法。项目页面：https：//gongjia0208.github.io/diffpose/。
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy. On the other hand, diffusion models have recently emerged as an effective tool for generating high-quality images from noise. Inspired by their capability, we explore a novel pose estimation framework (DiffPose) that formulates 3D pose estimation as a reverse diffusion process. We incorporate novel designs into our DiffPose to facilitate the diffusion process for 3D pose estimation: a pose-specific initialization of pose uncertainty distributions, a Gaussian Mixture Model-based forward diffusion process, and a context-conditioned reverse diffusion process. Our proposed DiffPose significantly outperforms existing methods on the widely used pose estimation benchmarks Human3.6M and MPI-INF-3DHP. Project page: https://gongjia0208.github.io/Diffpose/.
