Neural Local Inter-reflection Modeling for Garment Fold Rendering

초록

Realistic garment rendering requires simulating complex multi-bounce light paths within intricate fold geometries. In these regions, conventional path tracing is computationally expensive as light becomes trapped, necessitating high bounce counts for convergence. We observe that these local inter-reflections are highly localized and exhibit radiance patterns strongly correlated with local fold shapes. Based on these insights, we propose a neural local inter-reflection model that factorizes light transport into overall intensity and directional distribution. By learning the relationship between incident light, material properties, and a novel fold shape descriptor, our model approximates multi-bounce effects using a compact Spherical Harmonics representation. Our approach demonstrates strong generalization to unseen geometries and various fabric textures without retraining. Compared to full path tracing, our method significantly reduces rendering time while preserving high visual fidelity.

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