VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data (2207.09949v1)

Abstract: While monocular 3D pose estimation seems to have achieved very accurate results on the public datasets, their generalization ability is largely overlooked. In this work, we perform a systematic evaluation of the existing methods and find that they get notably larger errors when tested on different cameras, human poses and appearance. To address the problem, we introduce VirtualPose, a two-stage learning framework to exploit the hidden "free lunch" specific to this task, i.e. generating infinite number of poses and cameras for training models at no cost. To that end, the first stage transforms images to abstract geometry representations (AGR), and then the second maps them to 3D poses. It addresses the generalization issue from two aspects: (1) the first stage can be trained on diverse 2D datasets to reduce the risk of over-fitting to limited appearance; (2) the second stage can be trained on diverse AGR synthesized from a large number of virtual cameras and poses. It outperforms the SOTA methods without using any paired images and 3D poses from the benchmarks, which paves the way for practical applications. Code is available at https://github.com/wkom/VirtualPose.

• The paper introduces a novel two-stage framework that leverages synthetic data to overcome paired dataset limitations and enhance generalization.
• It employs abstract geometry representations to diversify input appearances and mitigate overfitting in 3D human pose models.
• Experimental results show significant performance gains over state-of-the-art methods across varied camera configurations and human poses.

Overview of VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data

The paper VirtualPose: Learning Generalizable 3D Human Pose Models from Virtual Data presents a novel approach to improve the generalization capabilities of monocular 3D pose estimation models. Despite achieving high accuracy on benchmark datasets, current monocular 3D pose estimation models exhibit decreased performance when tested against differing variables such as new camera configurations, diverse human poses, and varying appearances. This paper identifies these generalization shortcomings and introduces a solution through a proposed method named VirtualPose.

Contribution to Monocular 3D Pose Estimation

VirtualPose addresses generalization limitations by utilizing synthetic data to augment training procedures. The proposed two-stage learning framework allows for the creation and integration of infinite camera and pose variations, which effectively serve to enhance model training. This methodology is distinguished by its independence from paired images and 3D poses present in existing benchmark datasets, thereby offering a significant advantage.

Methodology

Stage 1: Abstract Geometry Representations (AGR)

The first stage of the VirtualPose framework transforms input images into abstract geometry representations (AGR). This transformation is strategically designed to diversify the training dataset by incorporating a range of appearances, which diminishes the risk of overfitting. The approach leverages various 2D datasets, ensuring a wide spectrum of appearances are represented without strict reliance on existing datasets.

Stage 2: Mapping to 3D Poses

In the second stage, the synthesized AGRs are mapped to 3D poses. This stage capitalizes on a large volume of synthetic data created from virtual cameras and poses. By decoupling the process into these two distinct stages, the framework concentrates on reducing the dependency on specifically paired image-3D pose data. This reduction in dependency is instrumental in overcoming traditional barriers faced by models relying heavily on limited, paired datasets.

Experimental Results

The efficacy of the VirtualPose framework is substantiated through a comprehensive set of experiments. The results indicate a performance increase over state-of-the-art (SOTA) methods concerning absolute 3D pose estimation using monocular images. The validation is demonstrated across diverse scenarios, highlighting the robustness and adaptability of the method to unseen camera poses, human appearances, and other domain shifts.

Implications and Future Directions

Practically, the VirtualPose framework offers significant advancements in the field of monocular 3D human pose estimation. By minimizing reliance on specific datasets while exploiting virtually generated training data, VirtualPose heralds an era of reduced barriers to generalization. Theoretically, this approach opens new avenues for exploring other computer vision applications using synthetic data to tackle generalization issues. Future research could expand this idea to encompass multi-person scenarios or extend the methodology to other tasks, such as action recognition or gesture interpretation, thereby further enhancing its practical applications in domains such as virtual reality, gaming, and human-computer interaction.