Robust Estimation of 3D Human Poses from a Single Image (1406.2282v1)

Abstract: Human pose estimation is a key step to action recognition. We propose a method of estimating 3D human poses from a single image, which works in conjunction with an existing 2D pose/joint detector. 3D pose estimation is challenging because multiple 3D poses may correspond to the same 2D pose after projection due to the lack of depth information. Moreover, current 2D pose estimators are usually inaccurate which may cause errors in the 3D estimation. We address the challenges in three ways: (i) We represent a 3D pose as a linear combination of a sparse set of bases learned from 3D human skeletons. (ii) We enforce limb length constraints to eliminate anthropomorphically implausible skeletons. (iii) We estimate a 3D pose by minimizing the $L_1$-norm error between the projection of the 3D pose and the corresponding 2D detection. The $L_1$-norm loss term is robust to inaccurate 2D joint estimations. We use the alternating direction method (ADM) to solve the optimization problem efficiently. Our approach outperforms the state-of-the-arts on three benchmark datasets.

• The paper introduces a robust framework that leverages sparse basis representation and L1-norm optimization to recover accurate 3D poses from one image.
• The method enforces anthropomorphic limb constraints to eliminate implausible configurations and improve accuracy despite 2D detection noise.
• Extensive evaluations on benchmark datasets demonstrate superior performance and efficiency, establishing a new baseline in 3D human pose research.

Robust Estimation of 3D Human Poses from a Single Image

The paper "Robust Estimation of 3D Human Poses from a Single Image" addresses the challenge of inferring 3D human pose configurations from a single image. This task is critical for applications in action recognition, human-computer interaction, and video surveillance. The proposed methodology builds upon existing 2D joint detection frameworks to address the inherent difficulties posed by the loss of depth information during the 2D to 3D projection process.

Methodology

The authors introduce an inventive approach that encompasses three key innovations:

1. Pose Representation: The method represents the 3D pose as a linear combination of a sparse set of bases derived from 3D human skeletons. This configuration allows for the reduction of ambiguities by situating the pose in a low-dimensional space, effectively using the inherent structural properties of human skeletons.
2. Limb Length Constraints: To ensure anthropomorphic plausibility, the model enforces constraints on limb lengths, thereby excluding configurations that do not comply with known human body proportions.
3. Optimization Using $L_1$ Norm: The authors propose the use of an $L_1$-norm loss function to measure the error between the projected 3D pose and the 2D detection outcomes. This technique is more resilient to inaccuracies within 2D joint estimation than traditional $L_2$-norm approaches. Specifically, its robustness to outliers or misdetections ensures better performance under uncertain 2D pose estimations.

To solve the posed optimization problem effectively, the alternating direction method (ADM) is employed, facilitating efficient updates and convergence.

Numerical Results and Comparisons

Extensive empirical evaluations conducted on three benchmark datasets — CMU motion dataset, HumanEva dataset, and the UvA 3D pose dataset — demonstrate the superiority of the proposed method over existing solutions. In particular, the approach consistently outperforms Ramakrishna et al.'s methodology by achieving notably lower average errors and demonstrating improved robustness across varying noise levels and camera viewpoints.

Additionally, the integration of a sparsity constraint in basis representation allows for a more compact and computationally efficient model without compromising accuracy. The method's ability to refine 2D pose detection by leveraging 3D constraints contributes a significant improvement in estimation accuracy, as highlighted by PCP scores and Euclidean distances on the UvA dataset.

Implications and Speculation on Future Developments

The implications of this research are manifold, most notably in improving real-time human pose estimation systems where computational efficiency and accuracy are paramount. The use of sparse basis representation and robust error metrics could be extended to other domains within 3D computer vision, such as object recognition and scene understanding, where similar challenges of depth ambiguity and accuracy prevail.

Looking forward, it would be beneficial to explore deeper integrations with machine learning models, potentially employing deep neural networks to learn both the bases and the features directly from data. Furthermore, extending this framework to handle dynamic scene changes in video sequences could significantly enhance robotic perception systems and autonomous vehicles.

Overall, the approach presented in this paper provides a robust framework for 3D human pose estimation and establishes a substantial foundation for subsequent advancements in this area. The consideration of both theoretical robustness and practical applicability underscores its potential for future impact in the field of computer vision.