AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression (2210.04014v1)

Abstract: Multi-person pose estimation generally follows top-down and bottom-up paradigms. Both of them use an extra stage ($\boldsymbol{e.g.,}$ human detection in top-down paradigm or grouping process in bottom-up paradigm) to build the relationship between the human instance and corresponding keypoints, thus leading to the high computation cost and redundant two-stage pipeline. To address the above issue, we propose to represent the human parts as adaptive points and introduce a fine-grained body representation method. The novel body representation is able to sufficiently encode the diverse pose information and effectively model the relationship between the human instance and corresponding keypoints in a single-forward pass. With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose. During inference, our proposed network only needs a single-step decode operation to form the multi-person pose without complex post-processes and refinements. We employ AdaptivePose for both 2D/3D multi-person pose estimation tasks to verify the effectiveness of AdaptivePose. Without any bells and whistles, we achieve the most competitive performance on MS COCO and CrowdPose in terms of accuracy and speed. Furthermore, the outstanding performance on MuCo-3DHP and MuPoTS-3D further demonstrates the effectiveness and generalizability on 3D scenes. Code is available at https://github.com/buptxyb666/AdaptivePose.

• The paper introduces a novel adaptive point representation that enables efficient regression of multi-person pose keypoints.
• It integrates a part perception module, an enhanced center-aware branch, and a two-hop regression branch to model complex body structures without heavy post-processing.
• Experiments on COCO, CrowdPose, and 3D datasets show that AdaptivePose++ outperforms existing methods in both accuracy and speed.

An Analysis of "AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression"

This essay presents a critical analysis of the paper titled "AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression," which outlines an advancement in the field of multi-person pose estimation through the introduction of a novel single-stage network. The research confronts the traditional computational complexities associated with multi-person pose estimation paradigms—namely the top-down and bottom-up approaches—by developing an innovative body representation framework.

Key Contributions and Framework Overview

One of the central contributions of this work is the introduction of a fine-grained body representation that articulates human parts as adaptive points. This approach effectively encodes diverse pose information and models the relationship between human instances and keypoints in a single-forward pass. The novelty of this representation is manifested in its ability to capture the intricate structural information of human poses through an adaptive point set, thus enhancing the localization and regression processes.

The proposed network, termed AdaptivePose, leverages this innovative representation within a compact framework that negates the need for complex post-processing stages, which are typically required in traditional methodologies. The architecture of AdaptivePose integrates three essential components:

1. Part Perception Module: This module regresses adaptive points pertinent to distinct human parts. By dynamically adjusting these points, the module accommodates diverse poses without the need for predefined or hand-crafted configurations.
2. Enhanced Center-aware Branch: This component conducts receptive field adaptation by harnessing the features of adaptive human-part related points. This approach ensures precise center localization, adjusting to the human body's scale and complex deformation.
3. Two-hop Regression Branch: Designed to regress keypoints, this branch employs adaptive part-related points as intermediary nodes. This methodology effectively models the interactions between the instance center and constituent keypoints using a two-hop regression strategy.

Empirical Evaluation and Results

The authors conducted substantial experiments using prominent datasets such as MS COCO and CrowdPose to validate the efficacy of AdaptivePose. The findings demonstrate significant improvements in accuracy, with the proposed method outperforming state-of-the-art competitors both in speed and precision. Notably, the performance on 3D datasets, such as MuCo-3DHP and MuPoTS-3D, underscores the generalizability and robustness of the network across two-dimensional and three-dimensional pose estimation tasks.

Implications

The theoretical and practical implications of AdaptivePose are profound. The ability to efficiently estimate multi-person poses in real-time unlocks numerous potential applications in fields such as human-computer interaction, augmented reality, and video surveillance. The network's efficiency and accuracy set a new benchmark for pose estimation models, potentially influencing future research trajectories in computer vision.

Future Directions

The AdaptivePose framework opens several avenues for further research. One potential direction includes integrating more sophisticated depth estimation methods to enhance 3D pose estimation performance further. Additionally, exploration into extending the framework's application to videos for temporal pose estimation could provide more holistic insights, particularly for action recognition and motion capture.

In conclusion, the introduction of AdaptivePose++ marks a significant step forward in the field of multi-person pose estimation. By effectively balancing computational efficiency with high-level accuracy, this research underscores the importance of innovative design paradigms in overcoming traditional limitations, thereby paving the way for more sophisticated applications in computer vision.