GazeD: Context-Aware Diffusion for Accurate 3D Gaze Estimation
This work addresses accurate 3D gaze estimation for applications like human-computer interaction, though it appears incremental by building on diffusion models.
The paper tackles 3D gaze estimation from a single RGB image by introducing GazeD, a diffusion-based method that jointly estimates 3D gaze and human pose, achieving state-of-the-art performance on benchmark datasets.
We introduce GazeD, a new 3D gaze estimation method that jointly provides 3D gaze and human pose from a single RGB image. Leveraging the ability of diffusion models to deal with uncertainty, it generates multiple plausible 3D gaze and pose hypotheses based on the 2D context information extracted from the input image. Specifically, we condition the denoising process on the 2D pose, the surroundings of the subject, and the context of the scene. With GazeD we also introduce a novel way of representing the 3D gaze by positioning it as an additional body joint at a fixed distance from the eyes. The rationale is that the gaze is usually closely related to the pose, and thus it can benefit from being jointly denoised during the diffusion process. Evaluations across three benchmark datasets demonstrate that GazeD achieves state-of-the-art performance in 3D gaze estimation, even surpassing methods that rely on temporal information. Project details will be available at https://aimagelab.ing.unimore.it/go/gazed.