title: Absolute Coordinates Make Motion Generation Easy
summary: State-of-the-art text-to-motion generation models rely on the
  kinematic-aware, local-relative motion representation popularized by HumanML3D,
  which encodes motion relative to the pelvis and to the previous frame with
  built-in redundancy. While this design simplifies training for earlier
  generation models, it introduces critical limitations for diffusion models and
  hinders applicability to downstream tasks. In this work, we revisit the motion
  representation and propose a radically simplified and long-abandoned
  alternative for text-to-motion generation: absolute joint coordinates in global
  space. Through systematic analysis of design choices, we show that this
  formulation achieves significantly higher motion fidelity, improved text
  alignment, and strong scalability, even with a simple Transformer backbone and
  no auxiliary kinematic-aware losses. Moreover, our formulation naturally
  supports downstream tasks such as text-driven motion control and
  temporal/spatial editing without additional task-specific reengineering and
  costly classifier guidance generation from control signals. Finally, we
  demonstrate promising generalization to directly generate SMPL-H mesh vertices
  in motion from text, laying a strong foundation for future research and
  motion-related applications.
paper_authors._id: 6836a0e48a36b9fa7f34ef2d
paper_authors.name: Zichong Meng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:56:36.129Z
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paper_authors.fullname: Zichong Meng
paper_authors.type: user
paper_authors.user: cr8br0ze
paper_authors._id: 6836a0e48a36b9fa7f34ef2e
paper_authors.name: Zeyu Han
paper_authors._id: 6836a0e48a36b9fa7f34ef2f
paper_authors.name: Xiaogang Peng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:28:47.091Z
paper_authors._id: 65dddb434b1db09751c164bd
paper_authors.avatarUrl: /avatars/f6b6fb4ae081785f070aad107c9e6449.svg
paper_authors.fullname: Xiaogang Peng
paper_authors.type: user
paper_authors.user: Eric-Peng
paper_authors._id: 6836a0e48a36b9fa7f34ef30
paper_authors.name: Yiming Xie
paper_authors._id: 6836a0e48a36b9fa7f34ef31
paper_authors.name: Huaizu Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T12:28:29.420Z
paper_authors._id: 6825fa72a85bada3f4c9230f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/4PIZwf33EKmy_VBXqpJMC.png
paper_authors.fullname: Huaizu Jiang
paper_authors.type: user
paper_authors.user: playerkk
paper_title: Absolute Coordinates Make Motion Generation Easy
paper_summary: State-of-the-art text-to-motion generation models rely on the
  kinematic-aware, local-relative motion representation popularized by HumanML3D,
  which encodes motion relative to the pelvis and to the previous frame with
  built-in redundancy. While this design simplifies training for earlier
  generation models, it introduces critical limitations for diffusion models and
  hinders applicability to downstream tasks. In this work, we revisit the motion
  representation and propose a radically simplified and long-abandoned
  alternative for text-to-motion generation: absolute joint coordinates in global
  space. Through systematic analysis of design choices, we show that this
  formulation achieves significantly higher motion fidelity, improved text
  alignment, and strong scalability, even with a simple Transformer backbone and
  no auxiliary kinematic-aware losses. Moreover, our formulation naturally
  supports downstream tasks such as text-driven motion control and
  temporal/spatial editing without additional task-specific reengineering and
  costly classifier guidance generation from control signals. Finally, we
  demonstrate promising generalization to directly generate SMPL-H mesh vertices
  in motion from text, laying a strong foundation for future research and
  motion-related applications.
paper_ai_summary: Absolute joint coordinates in global space improve motion fidelity, text alignment, and scalability for text-to-motion generation, supporting downstream tasks with a simple Transformer backbone.
paper_ai_keywords: kinematic-aware local-relative motion representation HumanML3D absolute joint coordinates global space diffusion models text-to-motion generation motion fidelity text alignment Transformer backbone downstream tasks text-driven motion control temporal editing spatial editing SMPL-H mesh vertices