mlsanityData to 7 Oct 2026

Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts

arXiv 1905.07862 ↗First posted 20 May 2019cs.CVcited by 19

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Abstract (arXiv)

Not all the human body parts have the same~degree of freedom~(DOF) due to the physiological structure. For example, the limbs may move more flexibly and freely than the torso does. Most of the existing 3D pose estimation methods, despite the very promising results achieved, treat the body joints equally and consequently often lead to larger reconstruction errors on the limbs. In this paper, we propose a progressive approach that explicitly accounts for the distinct DOFs among the body parts. We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated. Meanwhile, the high-DOF parts may, in turn, impose a constraint on where the low-DOF ones lie. As a result, parts with different DOFs supervise one another, yielding physically constrained and plausible pose-estimation results. To further facilitate the prediction of the high-DOF parts, we introduce a pose-attribute estimation, where the relative location of a limb joint with respect to the torso, which has the least DOF of a human body, is explicitly estimated and further fed to the joint-estimation module. The proposed approach achieves very promising results, outperforming the state of the art on several benchmarks.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 8; 2022: 3; 2023: 1; 2024: 3; 2025: 3; 2026: 1.

19 papers.

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  1. 2021Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flowscited by 84
  2. 2022DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion modelscited by 65
  3. 2021Context Modeling in 3D Human Pose Estimation: A Unified Perspectivecited by 37
  4. 2021Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Trackingcited by 37
  5. 2022PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervisioncited by 25
  6. 2021Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimationcited by 18
  7. 2021Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videoscited by 13
  8. 2021Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Liftingcited by 8
  9. 2024Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videoscited by 6
  10. 2024GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correctioncited by 5
  11. 2021Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Conceptcited by 4
  12. 2026QuaMo: Quaternion Motions for Vision-based 3D Human Kinematics Capturecited by 3
7 more
  1. 2021PONet: Robust 3D Human Pose Estimation via Learning Orientations Onlycited by 2
  2. 2025NanoHTNet: Nano Human Topology Network for Efficient 3D Human Pose Estimationcited by 2
  3. 2025Mamba-Driven Topology Fusion for Monocular 3D Human Pose Estimationcited by 2
  4. 2023Iterative Graph Filtering Network for 3D Human Pose Estimationcited by 1
  5. 2022(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimationcited by 0
  6. 2024Learning Human Motion from Monocular Videos via Cross-Modal Manifold Alignmentcited by 0
  7. 2025Adaptive graph Kolmogorov-Arnold network for 3D human pose estimationcited by 0