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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 5 Pith papers

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  4. Evolution of Video Generative Foundations

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    This survey traces video generation technology from GANs to diffusion models and then to autoregressive and multimodal approaches while analyzing principles, strengths, and future trends.

  5. A Survey of Mamba

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    The paper consolidates existing research on Mamba models, their architecture variants, adaptations to different data modalities, and applications across domains.

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