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ST-DiffEye: Diffusion-based Continuous Gaze Generation via Joint Scanpath-Trajectory Modeling

NeurIPS 2026 · Video › Video generation · approach: Text-to-motion diffusion · 0 citations (OpenAlex)

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Proposes ST-DiffEye, a diffusion transformer that jointly generates continuous eye trajectories and scanpaths by appending a fixation-index channel, and a CRPS-based distributional evaluation protocol for gaze generation.

Problem

Generating human gaze (scanpaths and continuous trajectories) under free-viewing and visual search, where gaze is highly variable across viewers, and existing evaluations (mean, best, KLD) fail to measure distributional accuracy and diversity.

Approach

Augment each trajectory token with a continuous fixation-index channel derived by aligning fixations to trajectory points, so one diffusion model jointly denoises both modalities; and wrap any sequence similarity metric in a CRPS proper scoring rule for evaluation.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: COCO-Search18 COCO-FreeView MIT1003

Benchmarks: COCO-Search18 COCO-FreeView MIT1003

Metrics: LD DFD DTW TDE MultiMatch ScanMatch Sequence Score Semantic Sequence Score CRPS KLD

Baselines: GazeFormer GazeXplain HAT TPP-Gaze DeepGaze III DiffEye ScanDiff

Builds on: ScanDiff DiffEye DDPM CRPS

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