ImageBrush: Learning Visual In-Context Instructions for Exemplar-Based
  Image Manipulation
Yasheng Sun Yifan Yang Houwen Peng Yifei Shen Yuqing Yang Han Hu Lili Qiu Hideki Koike
While language-guided image manipulation has made remarkable progress, the
challenge of how to instruct the manipulation process faithfully reflecting
human intentions persists. An accurate and comprehensive description of a
manipulation task using natural language is laborious and sometimes even
impossible, primarily due to the inherent uncertainty and ambiguity present in
linguistic expressions. Is it feasible to accomplish image manipulation without
resorting to external cross-modal language information? If this possibility
exists, the inherent modality gap would be effortlessly eliminated. In this
paper, we propose a novel manipulation methodology, dubbed ImageBrush, that
learns visual instructions for more accurate image editing. Our key idea is to
employ a pair of transformation images as visual instructions, which not only
precisely captures human intention but also facilitates accessibility in
real-world scenarios. Capturing visual instructions is particularly challenging
because it involves extracting the underlying intentions solely from visual
demonstrations and then applying this operation to a new image. To address this
challenge, we formulate visual instruction learning as a diffusion-based
inpainting problem, where the contextual information is fully exploited through
an iterative process of generation. A visual prompting encoder is carefully
devised to enhance the model's capacity in uncovering human intent behind the
visual instructions. Extensive experiments show that our method generates
engaging manipulation results conforming to the transformations entailed in
demonstrations. Moreover, our model exhibits robust generalization capabilities
on various downstream tasks such as pose transfer, image translation and video
inpainting.