Recent breakthroughs in understanding the human brain have revealed its impressive ability to efficiently process and interpret human thoughts, opening up possibilities for 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, progressively refining their final imagery synthesis. Through extensive experiments, we demonstrate the method ability to accurately instruct human brain signals with high fidelity. Our project will be publicly available on https://github.com/Sys-Nexus/DreamConnect

翻译：近期在理解人类大脑方面的突破揭示了其高效处理和解读人类思维的卓越能力，这为干预脑信号开辟了可能性。在本文中，我们旨在开发一个简洁的框架，利用其他模态（如自然语言）来翻译原始的梦境世界。我们提出了DreamConnect，它采用双流扩散框架来操纵视觉刺激下的脑信号。通过集成异步扩散策略，我们的框架建立了一个与人类梦境的有效接口，逐步优化其最终的图像合成。通过大量实验，我们证明了该方法能够以高保真度精确地指导人类脑信号。我们的项目将公开在 https://github.com/Sys-Nexus/DreamConnect 上提供。