Papers › FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration
Yu Rong, Takaaki Shiratori, Hanbyul Joo
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | 3DPW | FrankMocap | MPJPE | 94.3 | #92 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | FrankMocap | PA-MPJPE | 60 | #92 of 119 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | MPJPE, left hand | 13.2 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | MPJPE-14 | 62.3 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | PA V2V (mm), body only | 52.7 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | PA V2V (mm), left hand | 12.8 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | PA V2V (mm), whole body | 57.5 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | TR V2V (mm), body only | 80.1 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | TR V2V (mm), left hand | 32.1 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | TR V2V (mm), whole body | 76.9 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | mean P2S | 31.6 | #4 of 5 | Archive leaderboard | report |
| 3D Human Reconstruction | Expressive hands and faces dataset (EHF) | FrankMocap | median P2S | 19.2 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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