mlsanityData to 7 Oct 2026

Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space

arXiv 2406.11253 ↗First posted 17 Jun 2024cs.CVcited by 10

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Abstract (arXiv)

In this paper, we introduce a novel path to textitgeneral human motion generation by focusing on 2D space. Traditional methods have primarily generated human motions in 3D, which, while detailed and realistic, are often limited by the scope of available 3D motion data in terms of both the size and the diversity. To address these limitations, we exploit extensive availability of 2D motion data. We present Holistic-Motion2D, the first comprehensive and large-scale benchmark for 2D whole-body motion generation, which includes over 1M in-the-wild motion sequences, each paired with high-quality whole-body/partial pose annotations and textual descriptions. Notably, Holistic-Motion2D is ten times larger than the previously largest 3D motion dataset. We also introduce a baseline method, featuring innovative textitwhole-body part-aware attention and textitconfidence-aware modeling techniques, tailored for 2D underlinetext Text-drivunderlineEN whole-bounderlinetext Dy motion genunderlineERation, namely Tender. Extensive experiments demonstrate the effectiveness of Holistic-Motion2D and Tender in generating expressive, diverse, and realistic human motions. We also highlight the utility of 2D motion for various downstream applications and its potential for lifting to 3D motion. The page link is: https://holistic-motion2d.github.io.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 50 of its 56 references that are in the map. First posted on arXiv 17 Jun 2024.

Builds on

50 of 56 references.

References · 50

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.