mlsanityData to 7 Oct 2026

BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos

arXiv 2212.07401 ↗First posted 14 Dec 2022cs.CV cs.AIcited by 10

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Abstract (arXiv)

Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised keypoint discovery is a promising strategy for estimating 3D poses without annotations. However, current keypoint discovery approaches commonly process single 2D views and do not operate in the 3D space. We propose a new method to perform self-supervised keypoint discovery in 3D from multi-view videos of behaving agents, without any keypoint or bounding box supervision in 2D or 3D. Our method, BKinD-3D, uses an encoder-decoder architecture with a 3D volumetric heatmap, trained to reconstruct spatiotemporal differences across multiple views, in addition to joint length constraints on a learned 3D skeleton of the subject. In this way, we discover keypoints without requiring manual supervision in videos of humans and rats, demonstrating the potential of 3D keypoint discovery for studying behavior.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

30 of 50 references.

References · 30

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18 more
  1. 2017V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Mapcited by 102
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Citing papers per year: 2023: 4; 2024: 4; 2025: 2.

10 papers.