title: FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video
    Generation
summary: 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.
paper_authors._id: 6842e0874c601e90572c78a4
paper_authors.name: Huihan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:21.634Z
paper_authors._id: 6842d2f8668a1d103cca0b2b
paper_authors.avatarUrl: /avatars/bc61242617336ddf3e327534b396a2ce.svg
paper_authors.fullname: Huihan Wang
paper_authors.type: user
paper_authors.user: WTHH031230
paper_authors._id: 6842e0874c601e90572c78a5
paper_authors.name: Zhiwen Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:48:24.584Z
paper_authors._id: 638a2bdb34cf0480e9abd6a4
paper_authors.avatarUrl: /avatars/70e84c5c188688db8b29455061a410a1.svg
paper_authors.fullname: Zhiwen Yang
paper_authors.type: user
paper_authors.user: upyzwup
paper_authors._id: 6842e0874c601e90572c78a6
paper_authors.name: Hui Zhang
paper_authors._id: 6842e0874c601e90572c78a7
paper_authors.name: Dan Zhao
paper_authors._id: 6842e0874c601e90572c78a8
paper_authors.name: Bingzheng Wei
paper_authors._id: 6842e0874c601e90572c78a9
paper_authors.name: Yan Xu
paper_title: FEAT: Full-Dimensional Efficient Attention Transformer for Medical Video
    Generation
paper_summary: 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.
paper_ai_summary: FEAT, a full-dimensional efficient attention Transformer, addresses challenges in synthesizing high-quality dynamic medical videos by improving channel interactions, reducing computational complexity, and enhancing denoising guidance.
paper_ai_keywords: Transformer-based approaches sequential spatial-temporal-channel attention mechanisms linear-complexity design weighted key-value attention global channel attention residual value guidance module