This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem. Different from existing models that are either completely top-down or bottom-up, the proposed GPN introduces a novel strategy--it generates partitions for multiple persons from their global joint candidates and infers instance-specific joint configurations simultaneously. The GPN is favorably featured by low complexity and high accuracy of joint detection and re-organization. In particular, GPN designs a generative model that performs one feed-forward pass to efficiently generate robust person detections with joint partitions, relying on dense regressions from global joint candidates in an embedding space parameterized by centroids of persons. In addition, GPN formulates the inference procedure for joint configurations of human poses as a graph partition problem, and conducts local optimization for each person detection with reliable global affinity cues, leading to complexity reduction and performance improvement. GPN is implemented with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF, show the efficiency of GPN with new state-of-the-art performance.
Existing approaches following top-down strategy sequentially perform person detection and single-person pose estimation. @cite_27 , Gkioxari proposed to adopt the Generalized Hough Transform framework to first generate person proposals and then classify joint candidates based on the poselets. Sun @cite_13 presented a hierarchical part-based model for jointly person detection and pose estimation. Recently, deep learning techniques have been exploited to improve both person detection and single-person pose estimation. @cite_6 , Iqbal and Gall adopted Faster-RCNN @cite_2 based person detector and convolutional pose machine @cite_15 based joint detector for this task. Later, Fang @cite_19 utilized spatial transformer network @cite_25 and Hourglass network @cite_23 to further improve the quality of joint detections and partitions. Despite remarkable success, they suffer from limitations from early commitment and high joint detection complexity. Differently, the proposed GPN adopts a one-pass generative process for efficiently producing person detections with partitioned joint candidates, offering robustness to early commitment as well as low joint detection complexity.
@cite_6 @cite_19 @cite_27 @cite_23 @cite_2 @cite_15 @cite_13 @cite_25
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This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation1. This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation. Models and code available at this http URL. This paper proposes a novel Pose Partition Network (PPN) to address the challenging multi-person pose estimation problem. The proposed PPN is favorably featured by low complexity and high accuracy of joint detection and partition. In particular, PPN performs dense regressions from global joint candidates within a specific embedding space, which is parameterized by centroids of persons, to efficiently generate robust person detection and joint partition. Then, PPN infers body joint configurations through conducting graph partition for each person detection locally, utilizing reliable global affinity cues. In this way, PPN reduces computation complexity and improves multi-person pose estimation significantly. We implement PPN with the Hourglass architecture as the backbone network to simultaneously learn joint detector and dense regressor. Extensive experiments on benchmarks MPII Human Pose Multi-Person, extended PASCAL-Person-Part, and WAF show the efficiency of PPN with new state-of-the-art performance. In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos. Existing methods for multi-person pose estimation in images cannot be applied directly to this problem, since it also requires to solve the problem of person association over time in addition to the pose estimation for each person. We therefore propose a novel method that jointly models multi-person pose estimation and tracking in a single formulation. To this end, we represent body joint detections in a video by a spatio-temporal graph and solve an integer linear program to partition the graph into sub-graphs that correspond to plausible body pose trajectories for each person. The proposed approach implicitly handles occlusion and truncation of persons. Since the problem has not been addressed quantitatively in the literature, we introduce a challenging "Multi-Person PoseTrack" dataset, and also propose a completely unconstrained evaluation protocol that does not make any assumptions about the scale, size, location or the number of persons. Finally, we evaluate the proposed approach and several baseline methods on our new dataset.