ShadowGeneration

March 20, 2026 · View on GitHub

Embedding Physical Reasoning into Diffusion-Based Shadow Generation
Shilin Hu, Jingyi Xu, Akshat Dave, Dimitris Samaras, Hieu Le
arXiv, 2026. [arXiv]

This repository currently provides qualitative results.
Code and training/inference instructions will be released in a future update.

Overview

We propose a physics-grounded diffusion-based shadow generation pipeline that leverages monocular 3D geometry to recover a dominant light direction and derive a coarse shadow estimate via geometric ray-based reasoning to anchor shadow placement. By predicting confidence scores for both lighting and shadow estimates and using them to adaptively modulate conditioning during generation, our method produces photorealistic shadows with improved localization and geometric consistency.

Teaser

Results

Comparison with SOTA

Qualitative Results
Visual results in both BOS (with background reference object–shadow pairs) and BOS-free (single object–shadow pair). Our method produces higher image fidelity and more accurate shadow masks that better respect occluder–receiver–illumination relationships.

Citation

If you find this work useful, please cite:

@misc{hu2026embeddingphysicalreasoningdiffusionbased,
      title={Embedding Physical Reasoning into Diffusion-Based Shadow Generation}, 
      author={Shilin Hu and Jingyi Xu and Akshat Dave and Dimitris Samaras and Hieu Le},
      year={2026},
      eprint={2512.06174},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2512.06174}, 
}