Paint-Anything: 이미지 생성 및 편집을 위한 통합된 Any-Color 제어 기술
이 논문은 이미지 생성 및 편집 시 24-bit hex value를 사용하여 객체의 색상을 정밀하게 제어할 수 있는 Paint-Anything 프레임워크를 제안합니다. 연구진은 object grounding, perceptual color labeling, editing-pair synthesis를 통해 구축된 Paint-500K 데이터셋을 활용하여 shared hex-prompt interface를 학습시켰습니다. 또한, 그림자로 인한 색상 오차를 해결하기 위해 high-noise timestep에서 pure-color anchors를 사용하는 학습 전략을 도입했습니다.
Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and editing. Prior work has explored color generation, editing, and colorization, but often relies on dedicated color representations or specialized inference procedures. Advances in large language models offer a simpler starting point: even compact models can associate hex values with color semantics. We present Paint-Anything, which learns a shared hex-prompt interface for generation and editing through object-level color supervision. We develop a data pipeline that constructs Paint-500K from real images through object grounding, perceptual color labeling, and editing-pair synthesis. Since shadows make real-image labels only approximate colors, we complement this supervision with pure-color anchors whose pixels exactly match their paired hex values. These anchors are used only at high-noise timesteps, leaving low-noise training to natural imag