PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (2203.15625v1)

Abstract: Existing self-supervised 3D human pose estimation schemes have largely relied on weak supervisions like consistency loss to guide the learning, which, inevitably, leads to inferior results in real-world scenarios with unseen poses. In this paper, we propose a novel self-supervised approach that allows us to explicitly generate 2D-3D pose pairs for augmenting supervision, through a self-enhancing dual-loop learning framework. This is made possible via introducing a reinforcement-learning-based imitator, which is learned jointly with a pose estimator alongside a pose hallucinator; the three components form two loops during the training process, complementing and strengthening one another. Specifically, the pose estimator transforms an input 2D pose sequence to a low-fidelity 3D output, which is then enhanced by the imitator that enforces physical constraints. The refined 3D poses are subsequently fed to the hallucinator for producing even more diverse data, which are, in turn, strengthened by the imitator and further utilized to train the pose estimator. Such a co-evolution scheme, in practice, enables training a pose estimator on self-generated motion data without relying on any given 3D data. Extensive experiments across various benchmarks demonstrate that our approach yields encouraging results significantly outperforming the state of the art and, in some cases, even on par with results of fully-supervised methods. Notably, it achieves 89.1% 3D PCK on MPI-INF-3DHP under self-supervised cross-dataset evaluation setup, improving upon the previous best self-supervised methods by 8.6%. Code can be found at: https://github.com/Garfield-kh/PoseTriplet

• The paper presents a novel self-supervised PoseTriplet framework that co-evolves a 3D pose estimator, a reinforcement-learning based imitator, and a generative hallucinator.
• The framework leverages a dual-loop mechanism to iteratively refine predictions and enforce physical plausibility, achieving 89.1% 3D PCK on MPI-INF-3DHP with an 8.6% improvement over prior methods.
• Its innovative design reduces reliance on extensive labeled data, paving the way for robust applications in action recognition and mixed reality.

Overview of PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision

This essay discusses the paper "PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision," which proposes a novel approach for 3D human pose estimation that leverages a self-supervised framework to co-evolve an estimator, imitator, and hallucinator. The paper addresses the challenges inherent in self-supervised learning for pose estimation, particularly the reliance on weak supervision, which often results in suboptimal performance in real-world applications, especially with previously unseen poses.

Methodology

The core contribution of the paper is the PoseTriplet framework, which uniquely integrates three pivotal components: a pose estimator, a reinforcement-learning-based pose imitator, and a pose hallucinator, all interacting in a dual-loop learning strategy. This method departs from conventional self-supervised models that mainly depend on weak supervision, such as consistency loss, and instead utilizes a robust co-evolution scheme to harness self-generated 2D-3D pose pairs that enable more comprehensive supervision.

1. Pose Estimator: This component transforms input 2D poses into low-fidelity 3D outputs. Unlike traditional models that might rely on a large volume of labeled data for training, the PoseTriplet estimator is enhanced iteratively using diverse and plausible 3D data created within the dual-loop framework.
2. Pose Imitator: The imitator introduces physical plausibility through reinforcement learning, refining the estimations to enforce physical constraints, which addresses the physical implausibility often observed in previous approaches.
3. Pose Hallucinator: By leveraging generative motion techniques, the hallucinator enriches data diversity and serves as a context provider by generating realistic 3D pose sequences that enhance training further.

The dual-loop mechanism orchestrates an efficient exchange between these components to create a self-enhancing feedback system, thus enabling continuous improvements without the need for extensive 3D ground-truth data.

Results and Implications

The PoseTriplet framework demonstrates promising results on standard benchmarks such as H36M, 3DHP, and 3DPW. Notably, it achieves an impressive 89.1% 3D PCK on MPI-INF-3DHP under cross-dataset evaluation, with an 8.6% improvement over previously reported methods. These results position the PoseTriplet on par or even superior to some fully-supervised methods, showcasing its potential in overcoming the limitations of existing self-supervised approaches.

The implications of this work are significant both practically and theoretically. Practically, PoseTriplet advances the capabilities for 3D human pose estimation in diverse environments without reliance on costly, labor-intensive labeled data, demonstrating superior generalization—especially vital for deployment in less constrained applications like action recognition and mixed reality. Theoretically, the co-evolution strategy introduced here opens new pathways for integrated systems where multiple components can collectively enhance learning through self-generating data augmentation methods.

Future Directions

The paper proposes several potential future developments for AI research:

• Efficiency Improvements: The existing training process is resource-intensive, primarily due to the CPU-based implementation for the imitator and the RNN-based hallucinator. Therefore, exploring GPU-accelerated reinforcement learning and alternative architectures such as transformers may offer significant performance gains.
• Broader Applications: Extending the framework to other domains where replicating dynamic, realistic data in a cost-effective manner is challenging could further demonstrate its utility.
• Refinement of Hallucination Mechanisms: Exploring advanced motion synthesis techniques could enhance the diversity and richness of generated training data, thus further improving the robustness and applicability of the estimator across broader applications.

In conclusion, PoseTriplet represents a methodological advancement in self-supervised learning for 3D human pose estimation, offering a robust, more plausible approach to addressing key limitations in the field. Its innovative coupling of estimation, imitation, and hallucination through self-supervision holds considerable potential for further research and practical application within AI-driven pose estimation tasks.