Connecting dreams with visual brainstorming instruction - Yasheng Sun; Bohan Li; Mingchen Zhuge; Deng-Ping Fan; Salman Khan; Fahad Shahbaz Khan; Hideki Koike (Visual Intelligence)
Abstract Recent breakthroughs in understanding the human brain have revealed its impressive ability to efficiently process and interpret human thoughts, opening up the possibility of intervening in brain signals. In this paper, we aim to develop a straightforward framework that uses other modalities, such as natural language, to translate the original “dreamland”. We present DreamConnect, employing a dual-stream diffusion framework to manipulate visually stimulated brain signals. By integrating an asynchronous diffusion strategy, our framework establishes an effective interface with human “dreams”, and progressively refines their final image synthesis. Through extensive experiments, we demonstrate the efficacy of our method to accurately direct human brain signals in desired directions, ultimately enabling concept manipulation through direct manipulation of the functional magnetic resonance imaging (fMRI) signals. We hope that this work will motivate the use of brain signals in human-computer interaction applications.