IntrinsicEdit: Precise generative image manipulation in intrinsic space

SIGGRAPH 2025 (journal)

We propose a generative framework for diverse image-editing tasks, where precise manipulations can be performed in an intrinsic-image space and global-illumination effects are subsequently resolved automatically. Here we show a progressive transformation of an input image: ➀ We first remove the flowers and the vase from the albedo channel and then ➁ insert a new object in that channel. ➂ We replace the texture of another object before ➃ relighting the scene using a new irradiance channel. After each intrinsic-channel manipulation, we can render a physically plausible result. No single prior method can perform all these edits and provide similar levels of precision and identity preservation while delivering comparable image quality.

Abstract

Generative diffusion models have advanced image editing by delivering high-quality results through intuitive interfaces such as prompts, scribbles, and semantic drawing. However, these interfaces lack precise control, and associated editing methods often specialize in a single task. We introduce a versatile workflow for a range of editing tasks which operates in an intrinsic-image latent space, enabling semantic, local manipulation with pixel precision while automatically handling effects like reflections and shadows. We build on the RGB↔X diffusion framework and address its key deficiencies: the lack of identity preservation and the need to update multiple channels to achieve plausible results. We propose an edit-friendly diffusion inversion and prompt-embedding optimization to enable precise and efficient editing of only the relevant channels. Our method achieves identity preservation and resolves global illumination, without requiring task-specific model fine-tuning. We demonstrate state-of-the-art performance across a variety of tasks on complex images, including material adjustments, object insertion and removal, global relighting, and their combinations.

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BibTeX reference

@article{Lyu:2025:IntrinsicEdit,
  author = {Linjie Lyu and Valentin Deschaintre and Yannick Hold-Geoffroy and Miloš Hašan and Jae Shin Yoon and Thomas Leimkühler and Christian Theobalt and Iliyan Georgiev},
  title = {IntrinsicEdit: Precise generative image manipulation in intrinsic space},
  journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH)},
  year = {2025},
  volume = {44},
  number = {4},
  doi = {10.1145/3731173}
}