Presentation
Consecutive Frame Extrapolation with Predictive Sparse Shading
DescriptionHigh-frame-rate photorealistic rendering at modern displays is demanding. Existing frame generation and super-resolution techniques accelerate rendering by reducing rendering samples across space or time. However, uniform sample reduction often sacrifices quality, particularly in areas with complex details or dynamic shading. To address this, our approach accelerates rendering by selecting local areas: we reuse generated frames and re-shade essential areas to extrapolate frames. We introduce the Predictive Error-Flow-eXtrapolation Network (EFXNet), an architecture designed to concurrently predict errors, estimate flows, and generate frames. EFXNet predicts error scores and guides the future shading mask generation by leveraging temporal coherence. To handle inputs that may contain errors, the target-grid correlation module computes error-robust flow residuals by correlating reprojected multi-scale features. The temporally stable frame synthesis method distinctly processes historical geometric and lighting components using dedicated motion representations. Extensive experimental results show that, compared with state-of-the-art methods, our frame extrapolation method exhibits superior visual quality and temporal stability under a low rendering budget.
Event Type
Technical Papers
TimeMonday, 15 December 20255:13pm - 5:24pm HKT
LocationMeeting Room S426+S427, Level 4