Parametric Shadow Control for Portrait Generation in Text-to-Image Diffusion Models
International Conference on Computer Vision (ICCV) — 2025
Text-to-image diffusion models excel at generating diverse portraits, but lack intuitive shadow control. Existing editing approaches, as post-processing, struggle to offer effective manipulation across diverse styles, and either rely on expensive real-world light-stage data collection or require extensive computational resources for training. To address these limitations, we introduce Shadow Director, a method that extracts and manipulates hidden shadow attributes within well-trained diffusion models. Our approach uses a small estimation network that requires only a few thousand synthetic images and hours of training—no costly real-world light-stage data needed. Shadow Director enables parametric and intuitive control over shadow shape, placement, and intensity during portrait generation while preserving artistic integrity and identity across diverse styles.
@inproceedings{cai2025parametric,
title={Parametric shadow control for portrait generation in text-to-image diffusion models},
author={Cai, Haoming and Huang, Tsung-Wei and Gehlot, Shiv and Feng, Brandon Y and Shah, Sachin and Su, Guan-Ming and Metzler, Christopher},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={18207--18217},
year={2025}
}