Follow-Your-Mind: Towards Inversion-Free Brain-Driven Visual Context Synthesis and Editing

Deciphering human visual intent and manipulating visual con-tent via brain signals represents a significant milestone in neuro-generativeAI. Recent studies have demonstrated impressive image reconstructionfrom brain activity, yet they often rely on slow iterative diffusion inversionprocesses, makes it difficult to achieve real-time editing and fails to unifygeneration with precise context modification. Rectified Flow has recentlyemerged as a powerful solution for fast, deterministic generation. Inspiredby this, we propose MinD-Flow, a pioneering inversion-free framework forunified brain-driven synthesis and editing. It incorporates a Neuro-awareEncoder to achieve unified encoding of heterogeneous brain signals. Com-bined with the Neuro-Perceiver Bridge (NPB), it aligns brain semanticswith visual anchors, mapping the ‘Mind Space’ to the generative latentspace. Furthermore, the Decoupled Flow Guidance (DFG) and AdaptiveSoft Masking overcome the conflict between structural preservation andsemantic changes, enabling precise local editing without noise inversion.We introduce Brain-Gen Benchmark to standardize the evaluation ofNeuro-context/creation/editing tasks. Comprehensive experiments showSOTA performance, providing a versatile tool for BCI applications.