Whole-Body Human Pose Estimation in the Wild (2007.11858v1)

Abstract: This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet. As existing datasets do not have whole-body annotations, previous methods have to assemble different deep models trained independently on different datasets of the human face, hand, and body, struggling with dataset biases and large model complexity. To fill in this blank, we introduce COCO-WholeBody which extends COCO dataset with whole-body annotations. To our best knowledge, it is the first benchmark that has manual annotations on the entire human body, including 133 dense landmarks with 68 on the face, 42 on hands and 23 on the body and feet. A single-network model, named ZoomNet, is devised to take into account the hierarchical structure of the full human body to solve the scale variation of different body parts of the same person. ZoomNet is able to significantly outperform existing methods on the proposed COCO-WholeBody dataset. Extensive experiments show that COCO-WholeBody not only can be used to train deep models from scratch for whole-body pose estimation but also can serve as a powerful pre-training dataset for many different tasks such as facial landmark detection and hand keypoint estimation. The dataset is publicly available at https://github.com/jin-s13/COCO-WholeBody.

• The paper introduces a unified COCO-WholeBody dataset with 133 annotated landmarks for comprehensive human pose estimation in the wild.
• It presents ZoomNet, a novel model that mimics human vision by zooming into critical areas like hands and face to address scale variations.
• Results show an mAP of 0.541, demonstrating enhanced landmark localization and broad applicability in AR, VR, and interactive systems.

An Expert Analysis of "Whole-Body Human Pose Estimation in the Wild"

The paper "Whole-Body Human Pose Estimation in the Wild" by Sheng Jin et al. explores an advanced facet of pose estimation, focusing on the localization of dense landmarks across the entire human body. Unlike previous efforts constrained by separate datasets for face, hands, and body, this research introduces an innovative, consolidated dataset — COCO-WholeBody — with comprehensive annotations encompassing 133 distinct landmarks, thus facilitating a unified approach to whole-body pose estimation.

Dataset and Methodology

The COCO-WholeBody dataset significantly enriches existing resources by providing manual annotations for face, hands, body, and feet within wild environments. With attention to 68 face points, 42 hand points, and 23 body and feet points, the dataset encompasses a large variety of scenarios and poses, ensuring broad applicability in realistic settings. This resource is pivotal in addressing previous challenges faced by independent models combating dataset biases and complexity.

A novel model, termed ZoomNet, is introduced to address the hierarchical and scale variation issues inherent in whole-body estimation. ZoomNet leverages a single-network architecture to handle different scales across body parts efficiently, significantly outperforming pre-existing methodologies on the COCO-WholeBody dataset. The architecture is devised to prioritize resolution needs across different landmarks by focusing computed resources effectively by zooming into critical areas like hands and face.

Experimental Results

ZoomNet demonstrates notable advancements over previous methodologies, achieving impressive performance metrics on the COCO-WholeBody dataset. The paper reports a whole-body mean Average Precision (mAP) of 0.541, indicating significantly improved landmark localization accuracy. These results underscore the efficacy of a unified approach over traditional systems that deploy separate models for each set of body parts.

Furthermore, the dataset's utility extends beyond pose estimation. It serves as a robust pre-training ground for related tasks, such as facial landmark detection and hand keypoint estimation, promoting broader research synergies. Cross-dataset evaluations reveal that models pre-trained on COCO-WholeBody excel in performance when applied to distinct benchmarks, showcasing its versatility and cross-domain utility.

Implications and Future Directions

This research has substantial implications for applications in augmented and virtual reality, animation, and interactive systems requiring detailed human pose data. The introduction of COCO-WholeBody as an open resource propels further advancements in pose estimation by offering a comprehensive and consistently annotated benchmark, fostering an environment for innovation in both academia and industry.

The paper implies potential for future exploration in refining model efficiencies, particularly concerning hierarchical understanding and scale variance handling — areas where ZoomNet excels. Additionally, further development could focus on enhancing annotation methodologies and addressing the perspective of real-time applications, where computational efficiency becomes crucial.

In conclusion, this work represents a substantial step forward in whole-body pose estimation, offering a novel dataset, a compelling methodology, and strong empirical results. It sets a foundation for subsequent explorations in AI-based human pose analysis, with broad implications across diverse technological landscapes.