title: Seamless Human Motion Composition with Blended Positional Encodings
summary: Conditional human motion generation is an important topic with many
  applications in virtual reality, gaming, and robotics. While prior works have
  focused on generating motion guided by text, music, or scenes, these typically
  result in isolated motions confined to short durations. Instead, we address the
  generation of long, continuous sequences guided by a series of varying textual
  descriptions. In this context, we introduce FlowMDM, the first diffusion-based
  model that generates seamless Human Motion Compositions (HMC) without any
  postprocessing or redundant denoising steps. For this, we introduce the Blended
  Positional Encodings, a technique that leverages both absolute and relative
  positional encodings in the denoising chain. More specifically, global motion
  coherence is recovered at the absolute stage, whereas smooth and realistic
  transitions are built at the relative stage. As a result, we achieve
  state-of-the-art results in terms of accuracy, realism, and smoothness on the
  Babel and HumanML3D datasets. FlowMDM excels when trained with only a single
  description per motion sequence thanks to its Pose-Centric Cross-ATtention,
  which makes it robust against varying text descriptions at inference time.
  Finally, to address the limitations of existing HMC metrics, we propose two new
  metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt
  transitions.
paper_authors._id: 65dc16a1c5f46bf23bde5da2
paper_authors.name: German Barquero
paper_authors._id: 65dc16a1c5f46bf23bde5da3
paper_authors.name: Sergio Escalera
paper_authors._id: 65dc16a1c5f46bf23bde5da4
paper_authors.name: Cristina Palmero
paper_title: Seamless Human Motion Composition with Blended Positional Encodings
paper_summary: Conditional human motion generation is an important topic with many
  applications in virtual reality, gaming, and robotics. While prior works have
  focused on generating motion guided by text, music, or scenes, these typically
  result in isolated motions confined to short durations. Instead, we address the
  generation of long, continuous sequences guided by a series of varying textual
  descriptions. In this context, we introduce FlowMDM, the first diffusion-based
  model that generates seamless Human Motion Compositions (HMC) without any
  postprocessing or redundant denoising steps. For this, we introduce the Blended
  Positional Encodings, a technique that leverages both absolute and relative
  positional encodings in the denoising chain. More specifically, global motion
  coherence is recovered at the absolute stage, whereas smooth and realistic
  transitions are built at the relative stage. As a result, we achieve
  state-of-the-art results in terms of accuracy, realism, and smoothness on the
  Babel and HumanML3D datasets. FlowMDM excels when trained with only a single
  description per motion sequence thanks to its Pose-Centric Cross-ATtention,
  which makes it robust against varying text descriptions at inference time.
  Finally, to address the limitations of existing HMC metrics, we propose two new
  metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt
  transitions.
paper_ai_summary: FlowMDM is a diffusion-based model for generating long, continuous human motions guided by changing textual descriptions, achieving high accuracy and smoothness with novel positional encodings and attention mechanisms.
paper_ai_keywords: diffusion-based model Human Motion Compositions (HMC) Blended Positional Encodings global motion coherence smooth transitions Pose-Centric Cross-ATtention Peak Jerk Area Under the Jerk