title: Motion Mamba: Efficient and Long Sequence Motion Generation with
    Hierarchical and Bidirectional Selective SSM
summary: Human motion generation stands as a significant pursuit in generative
  computer vision, while achieving long-sequence and efficient motion generation
  remains challenging. Recent advancements in state space models (SSMs), notably
  Mamba, have showcased considerable promise in long sequence modeling with an
  efficient hardware-aware design, which appears to be a promising direction to
  build motion generation model upon it. Nevertheless, adapting SSMs to motion
  generation faces hurdles since the lack of a specialized design architecture to
  model motion sequence. To address these challenges, we propose Motion Mamba, a
  simple and efficient approach that presents the pioneering motion generation
  model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba
  (HTM) block to process temporal data by ensemble varying numbers of isolated
  SSM modules across a symmetric U-Net architecture aimed at preserving motion
  consistency between frames. We also design a Bidirectional Spatial Mamba (BSM)
  block to bidirectionally process latent poses, to enhance accurate motion
  generation within a temporal frame. Our proposed method achieves up to 50% FID
  improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets
  compared to the previous best diffusion-based method, which demonstrates strong
  capabilities of high-quality long sequence motion modeling and real-time human
  motion generation. See project website
  https://steve-zeyu-zhang.github.io/MotionMamba/
paper_authors._id: 65f11987e0aa03690e823794
paper_authors.name: Zeyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-03T07:42:15.670Z
paper_authors._id: 64ec877bb93654d4ca5c92e9
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paper_authors.fullname: Zeyu Zhang
paper_authors.type: user
paper_authors.user: SteveZeyuZhang
paper_authors._id: 65f11987e0aa03690e823795
paper_authors.name: Akide Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-13T13:51:41.537Z
paper_authors._id: 63ebc290d64e6436e2311074
paper_authors.avatarUrl: /avatars/13f08fbf3736e471e10bfc417377575e.svg
paper_authors.fullname: Akide Liu
paper_authors.type: user
paper_authors.user: Akide
paper_authors._id: 65f11987e0aa03690e823796
paper_authors.name: Ian Reid
paper_authors._id: 65f11987e0aa03690e823797
paper_authors.name: Richard Hartley
paper_authors._id: 65f11987e0aa03690e823798
paper_authors.name: Bohan Zhuang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-13T13:52:18.687Z
paper_authors._id: 65a8a828ef14f9e6037dfad2
paper_authors.avatarUrl: /avatars/55fdc0393b2e37ac998ddbdb1e1ff636.svg
paper_authors.fullname: Bohan Zhuang
paper_authors.type: user
paper_authors.user: BohanZ
paper_authors._id: 65f11987e0aa03690e823799
paper_authors.name: Hao Tang
paper_title: Motion Mamba: Efficient and Long Sequence Motion Generation with
    Hierarchical and Bidirectional Selective SSM
paper_summary: Human motion generation stands as a significant pursuit in generative
  computer vision, while achieving long-sequence and efficient motion generation
  remains challenging. Recent advancements in state space models (SSMs), notably
  Mamba, have showcased considerable promise in long sequence modeling with an
  efficient hardware-aware design, which appears to be a promising direction to
  build motion generation model upon it. Nevertheless, adapting SSMs to motion
  generation faces hurdles since the lack of a specialized design architecture to
  model motion sequence. To address these challenges, we propose Motion Mamba, a
  simple and efficient approach that presents the pioneering motion generation
  model utilized SSMs. Specifically, we design a Hierarchical Temporal Mamba
  (HTM) block to process temporal data by ensemble varying numbers of isolated
  SSM modules across a symmetric U-Net architecture aimed at preserving motion
  consistency between frames. We also design a Bidirectional Spatial Mamba (BSM)
  block to bidirectionally process latent poses, to enhance accurate motion
  generation within a temporal frame. Our proposed method achieves up to 50% FID
  improvement and up to 4 times faster on the HumanML3D and KIT-ML datasets
  compared to the previous best diffusion-based method, which demonstrates strong
  capabilities of high-quality long sequence motion modeling and real-time human
  motion generation. See project website
  https://steve-zeyu-zhang.github.io/MotionMamba/
paper_ai_keywords: state space models (SSMs) Mamba Motion Mamba Informatical Temporal Mamba (HTM) block Bidirectional Spatial Mamba (BSM) block U-Net architecture HumanML3D KIT-ML diffusion-based method FID (Fréchet Inception Distance)