Compositional Human Pose Regression (1704.00159v3)

Abstract: Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M and is competitive with state-of-the-art results on MPII.

• The paper introduces a novel bone-centric pose representation that stabilizes and enhances regression-based human pose estimation.
• It employs a compositional loss function to effectively encode long-range joint dependencies and enforce physical constraints.
• The unified approach achieves 59.1 mm average joint error on Human3.6M and 86.4% PCKh on MPII, outperforming conventional regression methods.

Compositional Human Pose Regression

The paper "Compositional Human Pose Regression" by Xiao Sun et al. addresses the limitations of regression-based methods in human pose estimation, compared to the more effective detection-based methods. The research introduces a structure-aware regression approach that leverages a novel pose representation using bones instead of joints. This represents a critical shift in pose estimation methodologies, aiming to incorporate structural information that previous regression techniques have neglected.

Key Contributions

1. Reparameterized Pose Representation: The paper proposes a representation focusing on bones rather than joints. This bone-centric approach is posited as more stable and easier to learn, providing a coherent structural relationship among components of the pose.
2. Compositional Loss Function: By introducing a compositional loss function, the approach effectively encodes long-range interactions in the pose, leveraging the joint connections. This is aimed to ensure the predicted poses respect physical constraints and dependencies between joints, which are typically overlooked in straightforward regression techniques.
3. Unified 2D and 3D Estimation: The method is designed to be general enough for both 2D and 3D pose estimation. Remarkably, the method allows for simultaneous training using both 2D and 3D datasets, an aspect not effectively addressed by prior approaches.

Numerical Results

The research reports significant advancements over the state-of-the-art benchmarks. Specifically, it achieves an average joint error of 59.1 mm on the Human3.6M dataset, marking approximately a 12% improvement. On the 2D MPII dataset, the approach achieves an 86.4% PCKh 0.5 score, putting it on par with detection-based methods while being the best regression-based method.

Implications

The implications of this research extend both theoretically and practically. Theoretically, it shifts the paradigm in human pose estimation towards integrating structural awareness in regression tasks. Practically, it offers a versatile tool applicable to both 2D and 3D scenarios, potentially simplifying pipelines that traditionally separate these tasks.

Future Developments

Future research can explore refining the compositional loss functions further to include more complex dependencies and constraints, potentially integrating real-time feedback for dynamic pose estimation in video sequences. Moreover, expanding the model's capability through deep learning advancements could further bridge the gap between detection and regression methods. Additionally, the implications of the bone-centric representation might be extended to other domains of computer vision, potentially influencing the design of algorithms that deal with hierarchical data structures.

This paper challenges existing norms in pose estimation, providing a robust foundation for further inquiry and application in the broader field of computer vision and artificial intelligence.