用旋转数据零样本估计人体姿态，不依赖用户体型差异。
Zero-shot Human Pose Estimation using Diffusion-based Inverse solvers
- 仅用传感器旋转信息+预训练扩散模型做逆问题求解
- 在不同体型用户间实现零样本泛化，无需重新训练
- 适合跨用户、低传感器数量的实时姿态追踪场景
姿态估计旨在追踪人体全身姿势，包括头、躯干、四肢等。在传感器数量有限的实际场景中，该任务极具挑战性。以往研究采用条件扩散模型，将姿态预测基于传感器提供的位置与旋转测量值。然而，这些方法在跨用户场景下泛化能力差，主要因位置测量受用户体型影响显著。本文将姿态估计建模为逆问题，提出InPose方法，仅使用旋转测量值作为条件，结合预训练扩散模型的先验，并通过由实测位置导出的似然项进行引导。该方法能针对任意用户生成最可能的姿态序列，以解释稀疏的体感测量数据，实现零样本泛化。
原文摘要 · Abstract (English)
Pose estimation refers to tracking a human's full body posture, including their head, torso, arms, and legs. The problem is challenging in practical settings where the number of body sensors are limited. Past work has shown promising results using conditional diffusion models, where the pose prediction is conditioned on both <location, rotation> measurements from the sensors. Unfortunately, nearly all these approaches generalize poorly across users, primarly because location measurements are highly influenced by the body size of the user. In this paper, we formulate pose estimation as an inverse problem and design an algorithm capable of zero-shot generalization. Our idea utilizes a pre-trained diffusion model and conditions it on rotational measurements alone; the priors from this model are then guided by a likelihood term, derived from the measured locations. Thus, given any user, our proposed InPose method generatively estimates the highly likely sequence of poses that best explains the sparse on-body measurements.
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