Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity

• URL: http://arxiv.org/abs/2405.03280v1
• Date: Mon, 6 May 2024 08:56:41 GMT
• Title: Animate Your Thoughts: Decoupled Reconstruction of Dynamic Natural Vision from Slow Brain Activity
• Authors: Yizhuo Lu, Changde Du, Chong Wang, Xuanliu Zhu, Liuyun Jiang, Huiguang He,
• Abstract summary: Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. This paper propose a two-stage model named Mind-Animator, which achieves state-of-the-art performance on three public datasets. We substantiate that the reconstructed video dynamics are indeed derived from fMRI, rather than hallucinations of the generative model.
• Score: 13.291585611137355
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Reconstructing human dynamic vision from brain activity is a challenging task with great scientific significance. The difficulty stems from two primary issues: (1) vision-processing mechanisms in the brain are highly intricate and not fully revealed, making it challenging to directly learn a mapping between fMRI and video; (2) the temporal resolution of fMRI is significantly lower than that of natural videos. To overcome these issues, this paper propose a two-stage model named Mind-Animator, which achieves state-of-the-art performance on three public datasets. Specifically, during the fMRI-to-feature stage, we decouple semantic, structural, and motion features from fMRI through fMRI-vision-language tri-modal contrastive learning and sparse causal attention. In the feature-to-video stage, these features are merged to videos by an inflated Stable Diffusion. We substantiate that the reconstructed video dynamics are indeed derived from fMRI, rather than hallucinations of the generative model, through permutation tests. Additionally, the visualization of voxel-wise and ROI-wise importance maps confirms the neurobiological interpretability of our model.

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