Joint Multi-Person Pose Estimation and Semantic Part Segmentation (1708.03383v1)

Abstract: Human pose estimation and semantic part segmentation are two complementary tasks in computer vision. In this paper, we propose to solve the two tasks jointly for natural multi-person images, in which the estimated pose provides object-level shape prior to regularize part segments while the part-level segments constrain the variation of pose locations. Specifically, we first train two fully convolutional neural networks (FCNs), namely Pose FCN and Part FCN, to provide initial estimation of pose joint potential and semantic part potential. Then, to refine pose joint location, the two types of potentials are fused with a fully-connected conditional random field (FCRF), where a novel segment-joint smoothness term is used to encourage semantic and spatial consistency between parts and joints. To refine part segments, the refined pose and the original part potential are integrated through a Part FCN, where the skeleton feature from pose serves as additional regularization cues for part segments. Finally, to reduce the complexity of the FCRF, we induce human detection boxes and infer the graph inside each box, making the inference forty times faster. Since there's no dataset that contains both part segments and pose labels, we extend the PASCAL VOC part dataset with human pose joints and perform extensive experiments to compare our method against several most recent strategies. We show that on this dataset our algorithm surpasses competing methods by a large margin in both tasks.

• The paper introduces a joint FCN and FCRF framework that integrates pose estimation and part segmentation for improved accuracy.
• The paper leverages a novel segment-joint smoothness term to fuse independent network outputs, ensuring better spatial consistency and reduced computational cost.
• The paper achieves a 10.6% boost in pose accuracy and a 1.5% improvement in segmentation, highlighting the benefits of multi-task learning in computer vision.

Joint Multi-Person Pose Estimation and Semantic Part Segmentation: A Technical Overview

This paper presents an integrated approach to tackle two interconnected problems in computer vision: multi-person pose estimation and semantic part segmentation. The authors propose a framework that jointly addresses these tasks using fully convolutional networks (FCNs) and a fully-connected conditional random field (FCRF). This integration leverages the complementary nature of these tasks, where pose estimation provides shape priors to part segmentation, and part segmentation offers spatial constraints to pose estimation.

The approach described in the paper begins by independently training two FCNs: the Pose FCN for extracting pose joint potentials and the Part FCN for generating semantic part potentials. The Pose FCN outputs pixel-wise joint score maps and joint neighbor score maps to determine the likelihood of joint presence and the expected spatial arrangement of joints, respectively. The Part FCN provides part segment score maps for human semantic part segmentation. Both networks employ the powerful architectures of CNNs to exploit large-scale annotated datasets effectively.

A novel aspect of the method is the fusion of these outputs using a fully-connected CRF with an innovative segment-joint smoothness term. This term enforces semantic and spatial consistency between the estimated pose joints and associated semantic parts, refining joint positions with increased accuracy. To further enhance the efficiency of the FCRF, human detection boxes are used to scope the inference process, reducing computational complexity significantly—up to fortyfold compared to analyzing an entire image.

In extending the PASCAL VOC part dataset to include human pose joints, the authors provided an empirical basis to validate their approach. The evaluation demonstrates substantial improvements over existing methods, with a notable 10.6% enhancement in pose estimation accuracy and a 1.5% increase in semantic part segmentation, along with accelerated computational speed.

These results have meaningful implications for applications reliant on accurate human pose understanding, such as action recognition, video surveillance, and fine-grained recognition tasks. The paper suggests that resolving pose estimation and part segmentation jointly can alleviate the intricacies of each task when addressed separately. The innovative use of joint and part segment correlations could inspire further research into multi-task learning frameworks, potentially extending beyond human pose analysis to other domains involving complex object interactions and spatial reasoning.

Overall, the proposed methodology signifies a promising direction in computer vision, demonstrating the synergistic benefits of addressing related tasks concurrently. As the field progresses, similar joint approaches could substantially improve and simplify solutions to intricate computer vision challenges.