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Matten: Video Generation with Mamba-Attention

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arxiv 2405.03025 v2 pith:6L6PHWV7 submitted 2024-05-05 cs.CV

Yu Gao , Jiancheng Huang , Xiaopeng Sun , Zequn Jie , Yujie Zhong , Lin Ma This is my paper
classification cs.CV
keywords mattenvideocontentgenerationmamba-attentionmodelmodelingperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce Matten, a cutting-edge latent diffusion model with Mamba-Attention architecture for video generation. With minimal computational cost, Matten employs spatial-temporal attention for local video content modeling and bidirectional Mamba for global video content modeling. Our comprehensive experimental evaluation demonstrates that Matten has competitive performance with the current Transformer-based and GAN-based models in benchmark performance, achieving superior FVD scores and efficiency. Additionally, we observe a direct positive correlation between the complexity of our designed model and the improvement in video quality, indicating the excellent scalability of Matten.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Audio time-frequency energy guides which video latents get recomputed during diffusion denoising, yielding up to 2.46x faster audio-driven video generation with competitive quality.

  2. SVG-EAR: Parameter-Free Linear Compensation for Sparse Video Generation via Error-aware Routing

    cs.CV 2026-03 conditional novelty 6.0 of 10

    Parameter-free centroid compensation plus error-aware block routing yields a better quality–density Pareto frontier for sparse attention in video DiTs than score-based sparsification.

  3. Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers

    cs.CV 2025-08 conditional novelty 6.0 of 10

    MiraMo turns a static image into a video using a linear-attention transformer that learns inter-frame motion residuals, with DCT-based noise refinement and a user-controllable dynamics knob.

  4. M4V: Multimodal Mamba for Efficient Text-to-Video Generation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    M4V shows a Mamba-based text-to-video model can roughly match attention-based PyramidFlow on VBench while cutting mixer-layer FLOPs by 45% at 768x1280.

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