MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior (1701.02354v2)

Abstract: Recovering 3D full-body human pose is a challenging problem with many applications. It has been successfully addressed by motion capture systems with body worn markers and multiple cameras. In this paper, we address the more challenging case of not only using a single camera but also not leveraging markers: going directly from 2D appearance to 3D geometry. Deep learning approaches have shown remarkable abilities to discriminatively learn 2D appearance features. The missing piece is how to integrate 2D, 3D and temporal information to recover 3D geometry and account for the uncertainties arising from the discriminative model. We introduce a novel approach that treats 2D joint locations as latent variables whose uncertainty distributions are given by a deep fully convolutional neural network. The unknown 3D poses are modeled by a sparse representation and the 3D parameter estimates are realized via an Expectation-Maximization algorithm, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Extensive evaluation on benchmark datasets shows that the proposed approach achieves greater accuracy over state-of-the-art baselines. Notably, the proposed approach does not require synchronized 2D-3D data for training and is applicable to "in-the-wild" images, which is demonstrated with the MPII dataset.

• The paper introduces a CNN-based framework that models 2D joints as latent variables and infers 3D poses using sparse recovery and an EM algorithm.
• It integrates temporal constraints to maintain smooth, consistent 3D pose estimations across monocular image sequences.
• The approach avoids the need for synchronized 2D-3D training data and outperforms state-of-the-art methods on datasets like Human3.6M and MPII.

Overview of "MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior"

The paper "MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior" explores the intricate problem of recovering 3D full-body human poses from monocular image sequences. Traditional motion capture systems typically rely on multiple cameras and markers to achieve high accuracy, which are impractical in several real-world situations. Here, the authors propose an innovative approach that circumvents these requirements by leveraging deep learning and geometric modeling, achieving superior performance in monocular settings.

Problem Formulation

Human pose estimation from a single camera input presents a significant challenge due to the inherent ambiguities and depth recovery limitations. Most existing solutions utilize either strict geometric methods or discriminative models that slack in combining temporal information efficiently. The advancements in Convolutional Neural Networks (CNNs) facilitate the accurate extraction of 2D pose information, yet translating this into reliable 3D data using a single input source remains an elusive task.

Methodological Contributions

• 2D to 3D Pose Estimation Framework: The authors propose a framework, MonoCap, that considers 2D joint locations as latent variables, estimating uncertainties with a CNN. The framework then applies a sparse representation model for 3D pose inference.
• Expectation-Maximization Optimization: An EM algorithm is employed to conveniently marginalize the 2D joint location uncertainties. This probabilistic approach is crucial in enhancing robustness against detector error and occlusions.
• Integration of Temporal Information: The approach incorporates temporal constraints, imposing smoothness in 3D pose estimation across frames, thus enhancing consistency and accuracy.
• Lack of Synchronized 2D-3D Training Data Requirement: Notably, the system does not require synchronized datasets for training, marking a substantial shift from traditional deep learning models necessitating abundant paired data.

Results

Comprehensive evaluations on multiple datasets, such as Human3.6M and MPII, demonstrate that the proposed model surpasses several state-of-the-art baselines in terms of accuracy. Specifically, it shows significant improvements in reconstructing 3D poses even when the 2D joints are unseen. The model generalizes well to "in-the-wild" scenarios, a testament to its practical applicability outside controlled environments.

Practical and Theoretical Implications

In practical terms, MonoCap represents a major step towards achieving real-world automation in systems relying on human motion capture without invasive hardware setups. Industries ranging from virtual reality to rehabilitation stand to benefit from such advancements. Theoretically, this work opens up new trajectories in the fusion of discriminative and generative modeling techniques, hinting at future developments that could include further integration of other sensory inputs and unsupervised learning paradigms.

Speculation on Future Developments

The field is moving towards end-to-end frameworks that obtain semantically rich 3D reconstructions directly from imagery. Incorporating additional sources of data, like IMUs or leveraging more sophisticated prior distributions and variational inference methods, might further enhance this model. With continuous refinement and faster hardware, achieving real-time performance with interpretability remains a desirable outcome. It lays the groundwork for more advanced modalities of human-environment interaction and ubiquitous computing.

Overall, "MonoCap" provides a robust and elegant solution to monocular 3D human motion capture, marking substantial progress in the field by weaving together probabilistic marginalization, sparse recovery, and CNN-based feature extraction.