title: MotionLab: Unified Human Motion Generation and Editing via the
    Motion-Condition-Motion Paradigm
summary: Human motion generation and editing are key components of computer graphics
  and vision. However, current approaches in this field tend to offer isolated
  solutions tailored to specific tasks, which can be inefficient and impractical
  for real-world applications. While some efforts have aimed to unify
  motion-related tasks, these methods simply use different modalities as
  conditions to guide motion generation. Consequently, they lack editing
  capabilities, fine-grained control, and fail to facilitate knowledge sharing
  across tasks. To address these limitations and provide a versatile, unified
  framework capable of handling both human motion generation and editing, we
  introduce a novel paradigm: Motion-Condition-Motion, which enables the unified
  formulation of diverse tasks with three concepts: source motion, condition, and
  target motion. Based on this paradigm, we propose a unified framework,
  MotionLab, which incorporates rectified flows to learn the mapping from source
  motion to target motion, guided by the specified conditions. In MotionLab, we
  introduce the 1) MotionFlow Transformer to enhance conditional generation and
  editing without task-specific modules; 2) Aligned Rotational Position Encoding}
  to guarantee the time synchronization between source motion and target motion;
  3) Task Specified Instruction Modulation; and 4) Motion Curriculum Learning for
  effective multi-task learning and knowledge sharing across tasks. Notably, our
  MotionLab demonstrates promising generalization capabilities and inference
  efficiency across multiple benchmarks for human motion. Our code and additional
  video results are available at: https://diouo.github.io/motionlab.github.io/.
paper_authors._id: 67a43546f6caedc30f9d8c71
paper_authors.name: Ziyan Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-06T14:15:01.599Z
paper_authors._id: 659faf1d874e583fed79d09b
paper_authors.avatarUrl: /avatars/178a18686426908b9496ce71f6550655.svg
paper_authors.fullname: Ziyan Guo
paper_authors.type: user
paper_authors.user: ZiyanGuo
paper_authors._id: 67a43546f6caedc30f9d8c72
paper_authors.name: Zeyu Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:38:48.101Z
paper_authors._id: 65fbc3c6f52ac1107f5b1677
paper_authors.avatarUrl: /avatars/b8373c039c3d978510b89d057bd9b5e8.svg
paper_authors.fullname: Zeyu Hu
paper_authors.type: user
paper_authors.user: zeyuhu
paper_authors._id: 67a43546f6caedc30f9d8c73
paper_authors.name: Na Zhao
paper_authors._id: 67a43546f6caedc30f9d8c74
paper_authors.name: De Wen Soh
paper_title: MotionLab: Unified Human Motion Generation and Editing via the
    Motion-Condition-Motion Paradigm
paper_summary: Human motion generation and editing are key components of computer graphics
  and vision. However, current approaches in this field tend to offer isolated
  solutions tailored to specific tasks, which can be inefficient and impractical
  for real-world applications. While some efforts have aimed to unify
  motion-related tasks, these methods simply use different modalities as
  conditions to guide motion generation. Consequently, they lack editing
  capabilities, fine-grained control, and fail to facilitate knowledge sharing
  across tasks. To address these limitations and provide a versatile, unified
  framework capable of handling both human motion generation and editing, we
  introduce a novel paradigm: Motion-Condition-Motion, which enables the unified
  formulation of diverse tasks with three concepts: source motion, condition, and
  target motion. Based on this paradigm, we propose a unified framework,
  MotionLab, which incorporates rectified flows to learn the mapping from source
  motion to target motion, guided by the specified conditions. In MotionLab, we
  introduce the 1) MotionFlow Transformer to enhance conditional generation and
  editing without task-specific modules; 2) Aligned Rotational Position Encoding}
  to guarantee the time synchronization between source motion and target motion;
  3) Task Specified Instruction Modulation; and 4) Motion Curriculum Learning for
  effective multi-task learning and knowledge sharing across tasks. Notably, our
  MotionLab demonstrates promising generalization capabilities and inference
  efficiency across multiple benchmarks for human motion. Our code and additional
  video results are available at: https://diouo.github.io/motionlab.github.io/.
paper_ai_keywords: rectified flows MotionFlow Transformer Aligned Rotational Position Encoding Task Specified Instruction Modulation Motion Curriculum Learning