Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. Although prior video reconstruction methods have made substantial progress, they still suffer from several limitations, including: (1) difficulty in simultaneously reconciling semantic (e.g. categorical descriptions), structure (e.g. size and color), and consistent motion information (e.g. order of frames); (2) low temporal resolution of fMRI, which poses a challenge in decoding multiple frames of video dynamics from a single fMRI frame; (3) reliance on video generation models, which introduces ambiguity regarding whether the dynamics observed in the reconstructed videos are genuinely derived from fMRI data or are hallucinations from generative model. To overcome these limitations, we propose a two-stage model named Mind-Animator. During the fMRI-to-feature stage, we decouple semantic, structure, and motion features from fMRI. Specifically, we employ fMRI-vision-language tri-modal contrastive learning to decode semantic feature from fMRI and design a sparse causal attention mechanism for decoding multi-frame video motion features through a next-frame-prediction task. In the feature-to-video stage, these features are integrated into videos using an inflated Stable Diffusion, effectively eliminating external video data interference. Extensive experiments on multiple video-fMRI datasets demonstrate that our model achieves state-of-the-art performance. Comprehensive visualization analyses further elucidate the interpretability of our model from a neurobiological perspective. Project page: https://mind-animator-design.github.io/.

Decoding dynamic visual information from brain activity remains challenging due to inter-subject neural heterogeneity, limited per-subject data availability, and the substantial temporal resolution gap between fMRI signals (0.5Hz) and video dynamics (30Hz). Current approaches face persistent challenges in addressing these temporal mismatches, demonstrate limited capacity to integrate subject-specific neural patterns with shared representational frameworks, and lack adequate semantic granularity for aligning neural responses with visual content. To bridge these gaps, we propose CrossMind-VL, a framework addressing these limitations through three innovations: (1) a Dynamic Temporal Alignment module that resolves temporal mismatches via exponentially decayed multi-frame fusion with adaptive decay coefficients; (2) a Brain Mixture-of-Experts architecture that combines subject-specific extractors with shared expert layers through parameter-efficient tri-modal contrastive learning; and (3) a Multi-perspective Semantic Hyper-Anchoring module that resolves cross-subject attention bias via multi-dimensional semantic decomposition, leveraging multimodal LLMs for fine-grained video semantic extraction-enabling the model to match individual attention patterns as different subjects naturally focus on distinct aspects of the same visual stimulus. This module boosts Top-10/Top-100 retrieval by 17.7%/6.6%. Experiments on two video-fMRI datasets demonstrate state-of-the-art performance, with 39%/30% improvements in Top-10/Top-100 accuracy over single-subject baselines and 27% gains against multi-subject models. The framework exhibits remarkable few-shot adaptability, retaining 97% performance when using only 10% training data for new subjects. Visualization analysis confirms this generalization capability stems from effective disentanglement of subject-specific and shared neural representations.