Monocular Real-time Full Body Capture with Inter-part Correlations (2012.06087v2)

Abstract: We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike previous works, our approach is jointly trained on multiple datasets focusing on hand, body or face separately, without requiring data where all the parts are annotated at the same time, which is much more difficult to create at sufficient variety. The possibility of such multi-dataset training enables superior generalization ability. In contrast to earlier monocular full body methods, our approach captures more expressive 3D face geometry and color by estimating the shape, expression, albedo and illumination parameters of a statistical face model. Our method achieves competitive accuracy on public benchmarks, while being significantly faster and providing more complete face reconstructions.

• The paper introduces a neural network that captures full-body, hand, and facial motions in real-time from a single RGB image.
• It employs an attention mechanism and inter-part feature composition to boost accuracy and computational efficiency, reducing processing times to 32.1 ms per frame.
• Multi-dataset training enables superior generalization across benchmarks, paving the way for enhanced AR, VR, and telepresence applications.

Monocular Real-time Full Body Capture with Inter-part Correlations

The paper "Monocular Real-time Full Body Capture with Inter-part Correlations" introduces a novel approach to human motion capture using monocular input, addressing several limitations present in previous methodologies. The focus of this research lies in capturing comprehensive human body shape and motion, encompassing hands and expressive facial features, all in real-time from a single RGB image. This advancement is realized through the development of a new neural network architecture which intelligently exploits correlations between various body parts to enhance computational efficiency and accuracy.

Technical Contributions

The research presents several noteworthy contributions:

1. Real-time Capture: The proposed approach is the first of its kind to facilitate real-time capture of 3D body, hands, and face using monocular cameras. Previous efforts in this domain predominantly required multi-view setups or depth cameras, which are impractical for many applications involving real-time interactions.
2. Network Architecture: A significant technical achievement of this paper is the design of a neural network that combines local and global features while exploiting inter-part correlations. Specifically, the network leverages high-level features from body data to boost the accuracy of hand pose estimation, thereby achieving a balance between speed and precision.
3. Multi-dataset Training: The framework utilizes diverse data modalities and separate training modules. This modular design allows for the independent training of body and hand pose estimators, enabling superior cross-dataset generalization. The approach circumvents the excessive data collection challenges typically associated with capturing all body parts simultaneously through dataset-specific training.

Key Methodological Insights

• Feature Composition: The use of high-frequency local features from hand regions combined with global features from body keypoint estimations represents a novel approach in this domain. This inter-part feature composition is crucial for maintaining real-time performance without sacrificing accuracy.
• Attention Mechanism: An attention mechanism is employed within the network to selectively utilize features according to the part-specific data present in the image, thereby refining the hand keypoint detection process during training.

Numerical Results and Generalization

The method is evaluated on several benchmarks for human motion capture tasks and demonstrates competitive accuracy with significantly reduced runtime compared to existing models. Importantly, the approach maintains a high level of detail in hand and facial model reconstructions, offering personalized and realistic outputs.

1. Runtime: The paper reports a total computation time of 32.1 ms per frame for full body capture, outstripping previous works that range from 60 ms to several seconds per frame, thereby facilitating real-time applications like VR and AR.
2. Generalization across Datasets: By employing multi-dataset training, the authors achieve superior generalization capabilities, demonstrating consistent performance across different evaluation datasets such as HM36M, MPII3D, and HUMBI, while leveraging only the relevant parts of different datasets.

Future Directions and Implications

The research opens paths for enhancing user experiences in interactive systems like augmented reality (AR), virtual reality (VR), and telepresence by offering more nuanced human motion capture capabilities without demanding complex multi-camera setups. Prospective research could delve into the integration of temporal information to further smooth motion transitions in video sequences, potentially leading to innovations in areas like live performance capture and immersive media production. Moreover, the personalized facial capture can be expanded for emotion tracking and adaptive avatar generation in social VR platforms, suggesting impactful applications as these technologies mature.

In conclusion, this paper lays a robust foundation for real-time monocular full-body capture, characterizing it as an efficient and scalable solution for a variety of interactive applications. Researchers and practitioners in computer vision and graphical computing can leverage these advancements to improve system performance and user interaction fidelity in diverse use cases.