HeadDiff: Exploring Rotation Uncertainty With Diffusion Models for Head Pose Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Reconstruction

Background:

• Head pose estimation is crucial for human-computer interaction and augmented reality.
• Existing methods struggle with rotation uncertainty, especially in unconstrained environments.
• Conventional image-to-pose techniques lack explicit modeling of the head pose rotational manifold.

Purpose of the Study:

• To introduce HeadDiff, a probabilistic regression diffusion model for robust head pose estimation.
• To explicitly address and mitigate rotation uncertainty in head pose estimation.
• To improve head pose estimation accuracy under diverse and challenging facial imaging conditions.

Main Methods:

• Formulating head pose estimation as a reverse diffusion process for progressive denoising on the rotation manifold.
• Employing an iterative refinement strategy for pose mapping.
• Utilizing an isotropic Gaussian distribution to encode rotation representation incoherence.
• Incorporating a cycle-consistent constraint to learn facial relationships for robustness against shape variations.

Main Results:

• HeadDiff effectively handles rotation uncertainty, particularly in wild facial conditions.
• The proposed diffusion process ensures pose rotation and refines mapping iteratively.
• Experimental results on multiple datasets show superior performance compared to state-of-the-art methods.
• The model achieves robust pose estimation despite diverse facial shape variations.

Conclusions:

• HeadDiff offers a significant advancement in head pose estimation accuracy and robustness.
• The probabilistic regression diffusion approach effectively models rotational uncertainty.
• The method demonstrates state-of-the-art performance without reliance on auxiliary datasets.