Related papers: IntrinsicEdit: Precise generative image manipulation in intrinsic space

IntrinsicEdit: Precise generative image manipulation in intrinsic space

URL: http://arxiv.org/abs/2505.08889v2
Date: Thu, 15 May 2025 09:19:01 GMT
Title: IntrinsicEdit: Precise generative image manipulation in intrinsic space
Authors: Linjie Lyu, Valentin Deschaintre, Yannick Hold-Geoffroy, Miloš Hašan, Jae Shin Yoon, Thomas Leimkühler, Christian Theobalt, Iliyan Georgiev,
Abstract summary: We introduce a versatile, generative workflow that operates in an intrinsic-image latent space.<n>We address key challenges of identity preservation and intrinsic-channel entanglement.<n>We enable precise, efficient editing with automatic resolution of global illumination effects.
Score: 53.404235331886255
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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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