mlsanityData to 7 Oct 2026

Placing Human Animations into 3D Scenes by Learning Interaction- and Geometry-Driven Keyframes

arXiv 2209.06314 ↗First posted 13 Sep 2022cs.CVcited by 8

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Abstract (arXiv)

We present a novel method for placing a 3D human animation into a 3D scene while maintaining any human-scene interactions in the animation. We use the notion of computing the most important meshes in the animation for the interaction with the scene, which we call "keyframes." These keyframes allow us to better optimize the placement of the animation into the scene such that interactions in the animations (standing, laying, sitting, etc.) match the affordances of the scene (e.g., standing on the floor or laying in a bed). We compare our method, which we call PAAK, with prior approaches, including POSA, PROX ground truth, and a motion synthesis method, and highlight the benefits of our method with a perceptual study. Human raters preferred our PAAK method over the PROX ground truth data 64.6% of the time. Additionally, in direct comparisons, the raters preferred PAAK over competing methods including 61.5% compared to POSA.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 30 of its 67 references that are in the map. First posted on arXiv 13 Sep 2022.

Builds on

30 of 67 references.

References · 30

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18 more
  1. 2021Robust Motion In-betweeningcited by 223
  2. 2021Neural Articulated Radiance Fieldcited by 196
  3. 2021BANMo: Building Animatable 3D Neural Models from Many Casual Videoscited by 195
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  18. 2020Pose to Seat: Automated Design of Body-Supporting Surfacescited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 1; 2026: 2.

8 papers.