LD²Scan: A Lightweight Dual-Temporal Constrained Scanpath Prediction Model for Omnidirectional Images

Predicting scanpaths in omnidirectional images (ODIs) is essential for simulating human gaze behaviors. However, current methods often struggle with long-term dependencies and exhibit high complexity, which limits their efficiency and scalability. To tackle these challenges, we propose LD²Scan, a lightweight diffusion-based model specifically designed for scanpath prediction in ODIs. It employs Efficient Equivariant (E4) convolution to enhance feature extraction from distorted ODIs while improving computational performance, thereby reducing resource demands. LD²Scan utilizes a dual-graph convolutional network (GCN) to enforce internal time constraints between fixations, integrating semantic-level GCN for sequential fixation modeling and image-level GCN to capture relationships across different images, enriching contextual information. We formulate the scanpath prediction issue as a conditional generation task, refining noisy scanpaths using features encoded by the dual-GCN and robust E4-processed features. Experimental results on several benchmark datasets demonstrate that LD²Scan outperforms existing methods in terms of both accuracy and efficiency.