Human Pose as Compositional Tokens

Abstract: Human pose is typically represented by a coordinate vector of body joints or their heatmap embeddings. While easy for data processing, unrealistic pose estimates are admitted due to the lack of dependency modeling between the body joints. In this paper, we present a structured representation, named Pose as Compositional Tokens (PCT), to explore the joint dependency. It represents a pose by M discrete tokens with each characterizing a sub-structure with several interdependent joints. The compositional design enables it to achieve a small reconstruction error at a low cost. Then we cast pose estimation as a classification task. In particular, we learn a classifier to predict the categories of the M tokens from an image. A pre-learned decoder network is used to recover the pose from the tokens without further post-processing. We show that it achieves better or comparable pose estimation results as the existing methods in general scenarios, yet continues to work well when occlusion occurs, which is ubiquitous in practice. The code and models are publicly available at https://github.com/Gengzigang/PCT.

• The paper introduces Pose as Compositional Tokens (PCT), a novel framework that recasts human pose estimation as a classification problem to reduce reconstruction error.
• The methodology transforms joint dependency modeling by encoding pose data into discrete tokens via an encoder, a shared codebook, and a decoder for improved accuracy.
• Experimental results show state-of-the-art performance on benchmarks like COCO and MPII, with notable robustness in occluded and dynamic environments.

Human Pose as Compositional Tokens

The paper, "Human Pose as Compositional Tokens" by Geng et al., presents a novel approach to human pose estimation within the field of computer vision, a task traditionally hampered by challenges such as occlusion and unrealistic pose estimation. The authors introduce a structured representation named Pose as Compositional Tokens (PCT) that addresses the limitations of existing coordinate and heatmap-based methods by explicitly modeling joint dependency and treating a pose as a composition of discrete tokens.

Methodology Overview

PCT transforms human pose estimation into a classification problem. The core idea is to represent a human pose by $M$ discrete tokens, each encoding a sub-structure of interdependent joints. This compositional design significantly reduces reconstruction error and enhances robustness, particularly under occlusion scenarios. The approach includes the following stages:

1. Encoder and Codebook Learning: The PCT model employs a compositional encoder to transform pose data into $M$ token features, each representing a sub-structure of the pose. These tokens are then quantized using a shared codebook, which allows the model to cover a comprehensive spectrum of pose variations without imposing unrealistic assumptions on joint dependencies.
2. Pose Estimation as Classification: Once the tokens are generated, the pose estimation task is recast as predicting the categories of these tokens from input images. A classifier outputs $M$ discrete indices, which are subsequently decoded into a full pose using a pre-trained decoder network. This network is fine-tuned jointly with the encoder and codebook to minimize reconstruction error.

Experimental Results

The presented method achieves comparable or superior accuracy on several benchmark datasets, including COCO, MPII, and Human3.6M, compared to state-of-the-art techniques. Noteworthy, PCT shows enhanced performance in occluded environments, as evidenced by results on datasets like CrowdPose and OCHuman. This reinforces the efficacy of the token-based dependency modeling over existing representations such as coordinate vectors and heatmaps.

Further, the efficacy of this method spans both 2D and 3D pose estimation tasks, with competitive results demonstrated on the Human3.6M dataset. The robust nature of the PCT framework, evidenced by its performance in diverse scenarios, marks an important step towards efficient and accurate human pose estimation.

Implications and Future Directions

The exploration of human pose as compositional tokens brings several implications. Practically, it contributes to improved accuracies in challenging scenarios like crowded and occluded environments, which are common in real-world applications. Theoretically, this structured approach invites a re-examination of dependency modeling in computer vision tasks, suggesting that more nuanced and context-aware relationships can be constructively explored.

Extending beyond the current task, the discrete nature of the token representation opens potential avenues for multi-modal learning applications, such as aligning and integrating pose estimation within larger frameworks involving textual or speech-based data processing. However, further exploration is required to expand the application domain of PCT and optimize it across varying computational and accuracy demands.

By introducing the concept of pose as compositional tokens, the paper by Geng et al. provides a concrete basis for future research into more holistic and efficient modeling of human joints, with far-reaching implications for computer vision applications in dynamic and complex environments.