MindPainter: Efficient Brain-Conditioned Painting of Natural Images via Cross-Modal Self-Supervised Learning

Abstract

Despite significant advancements in image and text conditional image editing, the exploration of using brain signals, which are more direct and personalized to reflect user intentions, remains limited. An intuitive method is to convert implicit brain signals into explicit representations such as images, which can then serve as prompts for editing. However, such two-stage method suffers from low inference efficiency, inaccurate brain interpretation, and unnatural editing results. In this paper, we apply brain signals of visual perception as prompts and propose a cross-modal self-supervised learning for natural image painting (MindPainter). This method achieves efficient and natural brain-conditioned image editing in a straightforward manner. MindPainter is trained for reconstruction from masked images directly with pseudo-brain signals, which is simulated by the proposed Pseudo Brain Generator. It facilitates efficient cross-modal integration. The proposed Brain Adapter further eliminates the gap in implicit space between modalities, ensuring accurate semantic interpretation of brain signals and coherent consolidation. Besides, the designed Multi-Mask Generation Policy enhances the generalization, realizing high-quality editing in various painting scenarios, including inpainting and outpainting. To the best of our knowledge, MindPainter is the first work to achieve efficient brain-conditioned image painting, providing potential for direct brain control in creative AI. The code and the link to the extended version will be available on GitHub.