mlsanityData to 14 Oct 2026

Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization

arXiv 2306.08861 ↗First posted 15 Jun 2023cs.CV cs.AIcited by 9

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Abstract (arXiv)

In this work, we proposed a new style-diverse dataset for the domain of motion style transfer. The motion dataset uses an industrial-standard human bone structure and thus is industry-ready to be plugged into 3D characters for many projects. We claim the challenges in motion style transfer and encourage future work in this domain by releasing the proposed motion dataset both to the public and the market. We conduct a comprehensive study on motion style transfer in the experiment using the state-of-the-art method, and the results show the proposed dataset's validity for the motion style transfer task.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

8 of 40 references.

References · 8

  1. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 2,010
  2. 2019Deep High-Resolution Representation Learning for Visual Recognitioncited by 1,420
  3. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,162
  4. 2021Robust Motion In-betweeningcited by 229
  5. 2020Unpaired Motion Style Transfer from Video to Animationcited by 94
  6. 2022Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phasescited by 72
  7. 2022Motion Puzzle: Arbitrary Motion Style Transfer by Body Partcited by 64
  8. 2020Real-Time Sign Language Detection using Human Pose Estimationcited by 29

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 6; 2026: 1.

9 papers.