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HeadDiff: Exploring Rotation Uncertainty With Diffusion Models for Head Pose Estimation
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DOI：10.1109/TIP.2024.3372457.png)
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Abstract
En 中文
In this paper, we propose a probabilistic regression diffusion model for head pose estimation, dubbed HeadDiff, which typically addresses the rotation uncertainty, especially when faces are captured in wild conditions. Unlike conventional image-to-pose methods which cannot explicitly establish the rotational manifold of head poses, our HeadDiff aims to ensure the pose rotation via the diffusion process and in parallel, refine the mapping process iteratively. Specifically, we initially formulate the head pose estimation problem as a reverse diffusion process, defining a paradigm for progressive denoising on the manifold, which explores the uncertainty by decomposing the large gap into intermediate steps. Moreover, our HeadDiff is equipped with an isotropic Gaussian distribution by encoding the incoherence information in our rotation representation. Finally, we learn the facial relationship of nearest neighbors with a cycle-consistent constraint for robust pose estimation versus diverse shape variations. Experimental results on multiple datasets demonstrate that our proposed method outperforms existing state-of-the-art techniques without auxiliary data.
Keywords:
Pose estimation
Solid modeling
Manifolds
Uncertainty
Three-dimensional displays
Faces
Rotation measurement
Head pose estimation
diffusion model
structural learning
biometrics
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W