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FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration (2108.06428v1)

Published 13 Aug 2021 in cs.CV

Abstract: 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.

Citations (172)

Summary

  • The paper presents FrankMocap, a modular system that integrates separate regressors for face, hand, and body pose estimation from monocular images.
  • It proposes three integration strategies, including a wrist network and optimization approach, to address conflicting outputs and enhance precision.
  • Experimental validation on datasets like 3DPW and MPII+NZSL shows improved V2V distances and competitive performance against state-of-the-art methods.

Evaluation of "FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration"

The paper presents FrankMocap, an integrated and modular system for 3D pose estimation of the entire human body, face, and hands from single monocular images. The development of FrankMocap addresses a significant gap in the field of human motion capture, aiming to provide accurate whole-body pose estimates by leveraging separate state-of-the-art regressors for different body components.

Methodological Approach

The FrankMocap system is characterized by its modular design, wherein individual regression modules are trained separately for the face, hands, and body. These modules are then integrated to form a cohesive estimate of the whole-body pose. The choice of using separate regressors allows the system to utilize the best available algorithms tailored for each body part without sacrificing accuracy. Specifically, the paper introduces three different integration strategies: a copy-paste approach that combines results directly, an optimization-based solution to refine pose estimate accuracy, and an intermediate approach through a wrist integration network that balances between computational efficiency and precision.

Experimental Validation

The system’s performance is validated across multiple datasets, including STB, RHD, and MPII+NZSL for hands, as well as the 3DPW dataset for body estimation. The evaluation also extends to using the publicly available EHF dataset, which encompasses whole-body motion captures. Across these datasets, the modular FrankMocap system demonstrates superior or comparable accuracy to current state-of-the-art methods, including SMPLify-X and ExPose.

Quantitatively, FrankMocap achieves significant improvements in V2V distance, a metric critical for evaluating 3D model accuracy, especially in hand and body pose estimation tasks. The optimization framework further refines integration results, showcasing its efficacy in enhancing overall pose accuracy beyond simple aggregation of module outputs.

Contributions and Implications

The contributions of this research are multifaceted. Firstly, the modular approach of FrankMocap circumvents the limitations posed by the absence of extensive whole-body annotated datasets. By using state-of-the-art part-specific modules, the system effectively employs existing datasets to better decode complex human poses. Secondly, the introduction of the wrist integration network, a neural network to efficiently adjust poses, signifies an innovative pathway towards resolving conflicting outputs between modules without extensive computations.

The implications of this research span practical applications in areas like augmented reality, biomechanics, and assistive technologies, where fast and accurate human pose estimation is paramount. Theoretically, FrankMocap’s modular design provides a flexible framework that can be expanded upon as individual component models improve.

Speculation on Future AI Developments

Future work may focus on refining the integration process and including dynamic temporal information to enhance the system's performance in real-time applications. The incorporation of multi-view or depth camera data might further improve robustness against occlusions and motion blur challenges. As deep learning models for pose estimation continue to evolve, FrankMocap’s modular framework stands ready to integrate these advancements, setting a precedent for future whole-body pose estimation systems.

In summary, FrankMocap presents a significant advancement in monocular 3D whole-body pose estimation, proving its worth by efficiently amalgamating state-of-the-art components into a versatile and robust system. The methodology and results of this paper warrant attention, as they furnish substantial possibilities for manipulating human pose data with increased accuracy and reduced latency.

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