title: MotionLCM: Real-time Controllable Motion Generation via Latent
    Consistency Model
summary: This work introduces MotionLCM, extending controllable motion generation to a
  real-time level. Existing methods for spatial control in text-conditioned
  motion generation suffer from significant runtime inefficiency. To address this
  issue, we first propose the motion latent consistency model (MotionLCM) for
  motion generation, building upon the latent diffusion model (MLD). By employing
  one-step (or few-step) inference, we further improve the runtime efficiency of
  the motion latent diffusion model for motion generation. To ensure effective
  controllability, we incorporate a motion ControlNet within the latent space of
  MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the
  vanilla motion space to control the generation process directly, similar to
  controlling other latent-free diffusion models for motion generation. By
  employing these techniques, our approach can generate human motions with text
  and control signals in real-time. Experimental results demonstrate the
  remarkable generation and controlling capabilities of MotionLCM while
  maintaining real-time runtime efficiency.
paper_authors._id: 6631c8aa86db0efb4076f139
paper_authors.name: Wenxun Dai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-02T09:04:04.779Z
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paper_authors.fullname: Dai-Wenxun
paper_authors.type: user
paper_authors.user: wxDai
paper_authors._id: 6631c8aa86db0efb4076f13a
paper_authors.name: Ling-Hao Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: Ling-Hao Chen
paper_authors.type: user
paper_authors.user: EvanTHU
paper_authors._id: 6631c8aa86db0efb4076f13b
paper_authors.name: Jingbo Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:14:39.043Z
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paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: Jingbo520
paper_authors._id: 6631c8aa86db0efb4076f13c
paper_authors.name: Jinpeng Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-05-02T08:14:47.023Z
paper_authors._id: 64ed85bfb6d4b4b02a9174f9
paper_authors.avatarUrl: /avatars/dff01f2333f27634b18ff9a9d293c516.svg
paper_authors.fullname: jinpengliu
paper_authors.type: user
paper_authors.user: jinpengliu
paper_authors._id: 6631c8aa86db0efb4076f13d
paper_authors.name: Bo Dai
paper_authors._id: 6631c8aa86db0efb4076f13e
paper_authors.name: Yansong Tang
paper_title: MotionLCM: Real-time Controllable Motion Generation via Latent
    Consistency Model
paper_summary: This work introduces MotionLCM, extending controllable motion generation to a
  real-time level. Existing methods for spatial control in text-conditioned
  motion generation suffer from significant runtime inefficiency. To address this
  issue, we first propose the motion latent consistency model (MotionLCM) for
  motion generation, building upon the latent diffusion model (MLD). By employing
  one-step (or few-step) inference, we further improve the runtime efficiency of
  the motion latent diffusion model for motion generation. To ensure effective
  controllability, we incorporate a motion ControlNet within the latent space of
  MotionLCM and enable explicit control signals (e.g., pelvis trajectory) in the
  vanilla motion space to control the generation process directly, similar to
  controlling other latent-free diffusion models for motion generation. By
  employing these techniques, our approach can generate human motions with text
  and control signals in real-time. Experimental results demonstrate the
  remarkable generation and controlling capabilities of MotionLCM while
  maintaining real-time runtime efficiency.
paper_ai_summary: MotionLCM generates real-time controllable human motions using text and control signals by extending latent diffusion models with a Motion ControlNet.
paper_ai_keywords: motion latent consistency model MotionLCM latent diffusion model Motion ControlNet real-time motion generation motion control signals human motions