IntrinsicEdit: Precise generative image manipulation in intrinsic space
Journal:
arXiv
Published Date:
May 13, 2025
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.
