Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky

Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-based approaches treat shadow generation as an image translation problem and capture these physical relationships only implicitly. This often results in misaligned or implausible shadows. Motivated by the physics of shadow formation, we introduce explicit geometric guidance for outdoor shadow generation. Given a composite image and a foreground object mask, we recover approximate scene geometry and estimate a dominant light direction to derive a coarse shadow estimate via geometric reasoning. While coarse, this estimate provides a spatial anchor for shadow placement. Because illumination cannot always be uniquely inferred from a single image, we predict confidence scores for both lighting and shadow cues and use them to regulate their influence during generation. These cues (shadow mask, light direction, and their confidence scores) condition a diffusion-based generator that refines the estimate into a realistic shadow. Experiments on DESOBAV2 show substantially improved shadow-region fidelity and localization, with an overall 23% lower shadow-region RMSE and 30% lower shadow-mask BER than the prior state-of-the-art method.