AI Research
GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning
Bridging the gap between synthetic data generation and precise gaze-control for reliable eye-tracking model training.
Diffusion models are increasingly used to generate synthetic training data, but precise label control remains difficult when the conditioning signal is low-dimensional and coarse. Text-conditioned images are judged by broad prompt consistency, whereas supervised training requires...
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ArXiv CS.CV
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ArXiv CS.CV
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Generated insight by OnAirToday AI
GazeDiT solves a critical bottleneck in synthetic training data by enabling diffusion models to generate images with gaze-accurate spatial positioning. This advancement allows for more robust eye-tracking system development without the need for expensive and time-consuming human data collection.
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