IntrinsicEdit: Precise generative image manipulation in intrinsic space

Linjie Lyu; Valentin Deschaintre; Yannick Hold-Geoffroy; Miloš Hašan; Jae Shin Yoon; Thomas Leimkühler; Christian Theobalt; Iliyan Georgiev

doi:10.1145/3731173

arXiv · 2505.08889

IntrinsicEdit: Precise generative image manipulation in intrinsic space

Linjie Lyu·Valentin Deschaintre·Yannick Hold-Geoffroy·Miloš Hašan·Jae Shin Yoon·Thomas Leimkühler·Christian Theobalt·Iliyan Georgiev

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Abstract

Generative diffusion models have advanced image editing with high-quality results and intuitive interfaces such as prompts and semantic drawing. However, these interfaces lack precise control, and the associated methods typically specialize on a single editing task. We introduce a versatile, generative workflow that operates in an intrinsic-image latent space, enabling semantic, local manipulation with pixel precision for a range of editing operations. Building atop the RGB-X diffusion framework, we address key challenges of identity preservation and intrinsic-channel entanglement. By incorporating exact diffusion inversion and disentangled channel manipulation, we enable precise, efficient editing with automatic resolution of global illumination effects -- all without additional data collection or model fine-tuning. We demonstrate state-of-the-art performance across a variety of tasks on complex images, including color and texture adjustments, object insertion and removal, global relighting, and their combinations.

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BibTeX RIS

Linjie Lyu, Valentin Deschaintre, Yannick Hold-Geoffroy, Miloš Hašan, Jae Shin Yoon, Thomas Leimkühler, Christian Theobalt, Iliyan Georgiev. 2025-05-13. IntrinsicEdit: Precise generative image manipulation in intrinsic space. https://doi.org/10.1145/3731173

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