Self-Supervised Multi-View 3D Gaze Target Estimation via Probabilistic Ray Marching
We present a self-supervised approach, Self-MVGTE, for estimating 3D gaze targets from multiple camera views. Unlike existing methods that independently estimate 2D gaze targets per camera view, Self-MVGTE predicts gaze targets directly in 3D space for the first time. Moreover, it does not require any ground-truth annotations from the target scene and uses only the multi-view input images from a calibrated camera setup, pseudo 2D gaze target labels from a monocular gaze target estimation model, and 3D gaze vectors from a monocular 3D gaze estimation model. A key challenge is that these pseudo labels are inherently noisy and multi-view inconsistent. To address this, we propose a probabilis...
| Authors | Keqi Chen, Vinkle Srivastav, Nicolas Padoy |
|---|---|
| Primary category | cs.CV |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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