FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
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Abstract (arXiv)
Most existing monocular 3D pose estimation approaches only focus on a single body part, neglecting the fact that the essential nuance of human motion is conveyed through a concert of subtle movements of face, hands, and body. In this paper, we present FrankMocap, a fast and accurate whole-body 3D pose estimation system that can produce 3D face, hands, and body simultaneously from in-the-wild monocular images. The core idea of FrankMocap is its modular design: We first run 3D pose regression methods for face, hands, and body independently, followed by composing the regression outputs via an integration module. The separate regression modules allow us to take full advantage of their state-of-the-art performances without compromising the original accuracy and reliability in practice. We develop three different integration modules that trade off between latency and accuracy. All of them are capable of providing simple yet effective solutions to unify the separate outputs into seamless whole-body pose estimation results. We quantitatively and qualitatively demonstrate that our modularized system outperforms both the optimization-based and end-to-end methods of estimating whole-body pose.
Key facts. Cited by 168 papers in the map (data through 7 Oct 2026). Builds on 58 of its 81 references that are in the map. First posted on arXiv 13 Aug 2021.
Builds on
58 of 81 references.
References · 58
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50 most cited of 58 All 58 (JSON)
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Citing papers per year: 2021: 8; 2022: 29; 2023: 45; 2024: 41; 2025: 25; 2026: 20.
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- 2026Reconstructing Humans and Objects in Interaction using Large Reconstruction Models27 Aug 2026
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- 2026Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding29 May 2026
- 2026REACH: Hand Pose Estimation from Room Corners21 May 2026
- 2026AVI-HT: Adaptive Vision-IMU Fusion for 3D Hand Tracking20 May 2026
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12 newest of 153 All 153 (JSON)