title: MotionStreamer: Streaming Motion Generation via Diffusion-based
    Autoregressive Model in Causal Latent Space
summary: This paper addresses the challenge of text-conditioned streaming motion
  generation, which requires us to predict the next-step human pose based on
  variable-length historical motions and incoming texts. Existing methods
  struggle to achieve streaming motion generation, e.g., diffusion models are
  constrained by pre-defined motion lengths, while GPT-based methods suffer from
  delayed response and error accumulation problem due to discretized non-causal
  tokenization. To solve these problems, we propose MotionStreamer, a novel
  framework that incorporates a continuous causal latent space into a
  probabilistic autoregressive model. The continuous latents mitigate information
  loss caused by discretization and effectively reduce error accumulation during
  long-term autoregressive generation. In addition, by establishing temporal
  causal dependencies between current and historical motion latents, our model
  fully utilizes the available information to achieve accurate online motion
  decoding. Experiments show that our method outperforms existing approaches
  while offering more applications, including multi-round generation, long-term
  generation, and dynamic motion composition. Project Page:
  https://zju3dv.github.io/MotionStreamer/
paper_authors._id: 67dd03fb2672aa3643252e8c
paper_authors.name: Lixing Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:40:07.470Z
paper_authors._id: 65220fedc709aaca9aa63061
paper_authors.avatarUrl: /avatars/36e56199fa8c98ff3430636567a0ed62.svg
paper_authors.fullname: Lixing Xiao
paper_authors.type: user
paper_authors.user: lxxiao
paper_authors._id: 67dd03fb2672aa3643252e8d
paper_authors.name: Shunlin Lu
paper_authors._id: 67dd03fb2672aa3643252e8e
paper_authors.name: Huaijin Pi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:52:07.238Z
paper_authors._id: 6836adaf8ec432fdc7e1c550
paper_authors.avatarUrl: /avatars/ca21e0022e43fae7eddcd4b58aa7406f.svg
paper_authors.fullname: Huaijin Pi
paper_authors.type: user
paper_authors.user: huaijinpi
paper_authors._id: 67dd03fb2672aa3643252e8f
paper_authors.name: Ke Fan
paper_authors._id: 67dd03fb2672aa3643252e90
paper_authors.name: Liang Pan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-01T15:59:42.952Z
paper_authors._id: 6630cc7e9ee8861dd0b9bdbd
paper_authors.avatarUrl: /avatars/01da7d60cbb107d58329bbb80d924eb4.svg
paper_authors.fullname: Liang Pan
paper_authors.type: user
paper_authors.user: lianganimation
paper_authors._id: 67dd03fb2672aa3643252e91
paper_authors.name: Yueer Zhou
paper_authors._id: 67dd03fb2672aa3643252e92
paper_authors.name: Ziyong Feng
paper_authors._id: 67dd03fb2672aa3643252e93
paper_authors.name: Xiaowei Zhou
paper_authors._id: 67dd03fb2672aa3643252e94
paper_authors.name: Sida Peng
paper_authors._id: 67dd03fb2672aa3643252e95
paper_authors.name: Jingbo Wang
paper_title: MotionStreamer: Streaming Motion Generation via Diffusion-based
    Autoregressive Model in Causal Latent Space
paper_summary: This paper addresses the challenge of text-conditioned streaming motion
  generation, which requires us to predict the next-step human pose based on
  variable-length historical motions and incoming texts. Existing methods
  struggle to achieve streaming motion generation, e.g., diffusion models are
  constrained by pre-defined motion lengths, while GPT-based methods suffer from
  delayed response and error accumulation problem due to discretized non-causal
  tokenization. To solve these problems, we propose MotionStreamer, a novel
  framework that incorporates a continuous causal latent space into a
  probabilistic autoregressive model. The continuous latents mitigate information
  loss caused by discretization and effectively reduce error accumulation during
  long-term autoregressive generation. In addition, by establishing temporal
  causal dependencies between current and historical motion latents, our model
  fully utilizes the available information to achieve accurate online motion
  decoding. Experiments show that our method outperforms existing approaches
  while offering more applications, including multi-round generation, long-term
  generation, and dynamic motion composition. Project Page:
  https://zju3dv.github.io/MotionStreamer/
paper_ai_summary: MotionStreamer framework enhances text-conditioned streaming motion generation by using continuous causal latents in a probabilistic autoregressive model, addressing issues of error accumulation and delayed response.
paper_ai_keywords: diffusion models GPT-based methods streaming motion generation continuous causal latent space probabilistic autoregressive model error accumulation online motion decoding