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DiTGaze system enhances few-shot gaze estimation using diffusion models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed DiTGaze, a novel system for few-shot gaze estimation that utilizes a two-stream 3D Gaussian Splatting backbone and a geometry-conditioned Diffusion Transformer. This approach reformulates the rendering stage to integrate Gaussian features with a latent diffusion model, incorporating conditioning for timestep, identity, and expression through Adaptive Layer Normalization. DiTGaze aims to reduce the burden of collecting extensive user-specific gaze data by improving few-shot personalization, showing significant error reduction on the ETH-XGaze dataset. AI

IMPACT This research could lead to more efficient and accurate gaze tracking systems with less user-specific data.

RANK_REASON This is a research paper detailing a new method for gaze estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DiTGaze system enhances few-shot gaze estimation using diffusion models

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This is a research paper detailing a new method for gaze estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Abiram Panchalingam, Indu P. Bodala, Stuart E. Middleton ·

    DiTGaze: Geometry-Conditioned Diffusion Augmentation for Few-Shot Gaze Estimation

    arXiv:2511.11231v2 Announce Type: replace Abstract: Accurate person-specific gaze estimation benefits from labeled user data, but collecting images that densely cover gaze directions is burdensome. We present DiTGaze, a calibration-efficient augmentation system that follows a two…

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