PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation (2105.02465v1)

Abstract: Existing 3D human pose estimators suffer poor generalization performance to new datasets, largely due to the limited diversity of 2D-3D pose pairs in the training data. To address this problem, we present PoseAug, a new auto-augmentation framework that learns to augment the available training poses towards a greater diversity and thus improve generalization of the trained 2D-to-3D pose estimator. Specifically, PoseAug introduces a novel pose augmentor that learns to adjust various geometry factors (e.g., posture, body size, view point and position) of a pose through differentiable operations. With such differentiable capacity, the augmentor can be jointly optimized with the 3D pose estimator and take the estimation error as feedback to generate more diverse and harder poses in an online manner. Moreover, PoseAug introduces a novel part-aware Kinematic Chain Space for evaluating local joint-angle plausibility and develops a discriminative module accordingly to ensure the plausibility of the augmented poses. These elaborate designs enable PoseAug to generate more diverse yet plausible poses than existing offline augmentation methods, and thus yield better generalization of the pose estimator. PoseAug is generic and easy to be applied to various 3D pose estimators. Extensive experiments demonstrate that PoseAug brings clear improvements on both intra-scenario and cross-scenario datasets. Notably, it achieves 88.6% 3D PCK on MPI-INF-3DHP under cross-dataset evaluation setup, improving upon the previous best data augmentation based method by 9.1%. Code can be found at: https://github.com/jfzhang95/PoseAug.

• The paper introduces a differentiable pose augmentation strategy that diversifies 2D-3D pose pairs to boost model generalization.
• It integrates a part-aware Kinematic Chain Space to assess local joint angle plausibility, ensuring augmented poses remain realistic.
• Extensive experiments show notable gains, achieving 88.6% 3D PCK on MPI-INF-3DHP and a 9.1% improvement over prior methods.

PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation

The paper presents "PoseAug," a novel framework aimed at addressing the generalization challenges inherent in 3D human pose estimation models. Traditional 3D pose estimators often suffer from poor performance when applied to new datasets, primarily due to the insufficient diversity of 2D-3D pose pairs in the training datasets. PoseAug seeks to mitigate this issue through a learning-based, differentiable augmentation strategy that enhances the diversity of the training data.

Key Contributions

PoseAug introduces a pose augmentor that operates through differentiable operations to adjust various geometric factors such as posture, body size, view point, and position of a pose. This differentiable nature permits the augmentor to be jointly optimized with the 3D pose estimator, enabling the generation of more varied and challenging poses via online learning.

Another distinctive component of PoseAug lies in its part-aware Kinematic Chain Space (KCS), which evaluates the plausibility of joint angles locally. This ensures that the augmented data remains realistic and useful for training, unlike offline methods which may generate less effective data due to their lack of interaction with model training.

Methodology and Results

The PoseAug framework is flexible and designed to be seamlessly integrated with various 3D pose estimators. The framework was evaluated through extensive experiments, which demonstrated significant performance improvements under both intra-scenario and cross-scenario conditions. Specifically, PoseAug achieved notable success on the MPI-INF-3DHP dataset, achieving 88.6% 3D PCK under a cross-dataset evaluation setup, marking a 9.1% improvement over previous data augmentation methods.

Implications and Future Work

By enhancing the diversity of training data in a dynamic manner, PoseAug effectively improves the generalization capabilities of 3D pose estimators. The proposed approach reduces reliance on external datasets while generating training poses with varied and realistic geometric features.

Future directions could explore further refinement of the augmentation strategies and possibly incorporate additional aspects of human anatomy or environmental factors to improve model resilience to even more diverse scenarios. Additionally, research could focus on optimizing the computational efficiency of the PoseAug framework to facilitate real-time applications.

In summary, PoseAug offers a significant step forward in the field of 3D human pose estimation by ensuring models are better prepared to handle unseen data, thus broadening the scope of practical applications in fields like action recognition and human-robot interaction.