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arXiv Computer Vision By Dongze Wu, David Colmenares, Fengting Yang, Jogendra Nath Kundu, Yao Xie, Ali Behrooz, Conny Lu

GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning

Read the original on arXiv Computer Vision →

GazeDiT is a diffusion model that generates synthetic eye images conditioned on a precise 4‑dimensional gaze vector by using a spatial condition derived from pupil and iris geometry. During training, a frozen SegFormer extracts this geometry from real images, while inference employs a physical eye renderer to produce gaze‑consistent geometries without needing a source image. The model achieves lower tail gaze‑label error than other diffusion baselines and improves downstream eye‑tracking accuracy, reducing error on challenging cases from 3.05° to 2.80°.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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