FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation

• URL: http://arxiv.org/abs/2506.04956v1
• Date: Thu, 05 Jun 2025 12:31:02 GMT
• Title: FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video Generation
• Authors: Huihan Wang, Zhiwen Yang, Hui Zhang, Dan Zhao, Bingzheng Wei, Yan Xu,
• Abstract summary: We propose FEAT, a full-dimensional efficient attention Transformer for high-quality dynamic medical videos.<n>We evaluate FEAT on standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only 23% of the parameters of the state-of-the-art model Endora, achieves comparable or even superior performance.
• Score: 14.903360987684483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthesizing high-quality dynamic medical videos remains a significant challenge due to the need for modeling both spatial consistency and temporal dynamics. Existing Transformer-based approaches face critical limitations, including insufficient channel interactions, high computational complexity from self-attention, and coarse denoising guidance from timestep embeddings when handling varying noise levels. In this work, we propose FEAT, a full-dimensional efficient attention Transformer, which addresses these issues through three key innovations: (1) a unified paradigm with sequential spatial-temporal-channel attention mechanisms to capture global dependencies across all dimensions, (2) a linear-complexity design for attention mechanisms in each dimension, utilizing weighted key-value attention and global channel attention, and (3) a residual value guidance module that provides fine-grained pixel-level guidance to adapt to different noise levels. We evaluate FEAT on standard benchmarks and downstream tasks, demonstrating that FEAT-S, with only 23\% of the parameters of the state-of-the-art model Endora, achieves comparable or even superior performance. Furthermore, FEAT-L surpasses all comparison methods across multiple datasets, showcasing both superior effectiveness and scalability. Code is available at https://github.com/Yaziwel/FEAT.

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