Human body part parsing refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part parsing methods achieve good performances, but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs required for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part parsing framework. The basic idea is to train a parsing network with pose generated part priors that has blank uncertain regions on estimated boundaries, and use an iterative refinement module to generate new supervision and predictions on these regions. When sufficient extra weak supervisions are available, our weakly-supervised results (62.0 mIoU) on Pascal-Person-Part are comparable to the fully supervised state-of-the-art results (63.6 mIoU). Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. In addition, we show that the proposed framework can be extended to other keypoint-supervised part parsing tasks such as face parsing.
Human body part parsing. Human body part parsing is the task of generating body part masks based on human structures. An Auto-Zoom Network @cite_21 is proposed to focus on certain body part regions. Xia @cite_9 propose to jointly conduct body part parsing and pose estimation, and show that the two complementary tasks could help each other. Fang @cite_34 propose a data augmentation method for part parsing based on pose similarities. Part parsing is related to human parsing @cite_1 @cite_28 @cite_17 @cite_35 @cite_23 @cite_13 , as both tasks aim to predict pixel level semantic masks for human parts. The major difference is that body part parsing focuses on predicting part masks that directly reflects body structures, for example regions such as upper lower arms. On the contrary, human parsing contains clothing and object classes such as sunglasses, hats, coats and etc. These classes are not directly related to human structures and are more appearance based.
@cite_35 @cite_28 @cite_9 @cite_21 @cite_1 @cite_23 @cite_34 @cite_13 @cite_17
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Recognizing humans, estimating their pose and segmenting their body parts are key to high-level image understanding. Because humans are highly articulated, the range of deformations they undergo makes this task extremely challenging. Previous methods have focused largely on heuristics or pairwise part models in approaching this problem. We propose a bottom-up parsing of increasingly more complete partial body masks guided by a parse tree. At each level of the parsing process, we evaluate the partial body masks directly via shape matching with exemplars, without regard to how the parses are formed. The body is evaluated as a whole, not the sum of its constituent parses, unlike previous approaches. Multiple image segmentations are included at each of the levels of the parsing, to augment existing parses or to introduce ones. Our method yields both a pose estimate as well as a segmentation of the human. We demonstrate competitive results on this challenging task with relatively few training examples on a dataset of baseball players with wide pose variation. Our method is comparatively simple and could be easily extended to other objects. Human body part parsing, or human semantic part segmentation, is fundamental to many computer vision tasks. In conventional semantic segmentation methods, the ground truth segmentations are provided, and fully convolutional networks (FCN) are trained in an end-to-end scheme. Although these methods have demonstrated impressive results, their performance highly depends on the quantity and quality of training data. In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations. Our key idea is to exploit the anatomical similarity among human to transfer the parsing results of a person to another person with similar pose. Using these estimated results as additional training data, our semi-supervised model outperforms its strong-supervised counterpart by 6 mIOU on the PASCAL-Person-Part dataset [6], and we achieve state-of-the-art human parsing results. Our approach is general and can be readily extended to other object animal parsing task assuming that their anatomical similarity can be annotated by keypoints. The proposed model and accompanying source code will be made publicly available. In this work, the human parsing task, namely decomposing a human image into semantic fashion body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches @math percent by our ATR framework, significantly higher than @math percent based on the state-of-the-art algorithm [28] . We consider the problem of human parsing with part-based models. Most previous work in part-based models only considers rigid parts (e.g. torso, head, half limbs) guided by human anatomy. We argue that this representation of parts is not necessarily appropriate for human parsing. In this paper, we introduce hierarchical poselets–a new representation for human parsing. Hierarchical poselets can be rigid parts, but they can also be parts that cover large portions of human bodies (e.g. torso + left arm). In the extreme case, they can be the whole bodies. We develop a structured model to organize poselets in a hierarchical way and learn the model parameters in a max-margin framework. We demonstrate the superior performance of our proposed approach on two datasets with aggressive pose variations. Parsing human regions into semantic parts, e.g., body, head and arms etc., from a random natural image is challenging while fundamental for computer vision and widely applicable in industry. One major difficulty to handle such a problem is the high flexibility of scale and location of a human instance and its corresponding parts, making the parsing task either lack of boundary details or suffer from local confusions. To tackle such problems, in this work, we propose the "Auto-Zoom Net" (AZN) for human part parsing, which is a unified fully convolutional neural network structure that: (1) parses each human instance into detailed parts. (2) predicts the locations and scales of human instances and their corresponding parts. In our unified network, the two tasks are mutually beneficial. The score maps obtained for parsing help estimate the locations and scales for human instances and their parts. With the predicted locations and scales, our model "zooms" the region into a right scale to further refine the parsing. In practice, we perform the two tasks iteratively so that detailed human parts are gradually recovered. We conduct extensive experiments over the challenging PASCAL-Person-Part segmentation, and show our approach significantly outperforms the state-of-art parsing techniques especially for instances and parts at small scale. In addition, we perform experiments for horse and cow segmentation and also obtain results which are considerably better than state-of-the-art methods (by over 5 )., which is contribued by the proposed iterative zooming process. Parsing human into semantic parts is crucial to human-centric analysis. In this paper, we propose a human parsing pipeline that uses pose cues, i.e., estimates of human joint locations, to provide pose-guided segment proposals for semantic parts. These segment proposals are ranked using standard appearance cues, deep-learned semantic feature, and a novel pose feature called pose-context. Then these proposals are selected and assembled using an And-Or graph to output a parse of the person. The And-Or graph is able to deal with large human appearance variability due to pose, choice of clothes, etc. We evaluate our approach on the popular Penn-Fudan pedestrian parsing dataset, showing that it significantly outperforms the state-of-the-arts, and perform diagnostics to demonstrate the effectiveness of different stages of our pipeline. Instance-level human parsing towards real-world human analysis scenarios is still under-explored due to the absence of sufficient data resources and technical difficulty in parsing multiple instances in a single pass. Several related works all follow the “parsing-by-detection” pipeline that heavily relies on separately trained detection models to localize instances and then performs human parsing for each instance sequentially. Nonetheless, two discrepant optimization targets of detection and parsing lead to suboptimal representation learning and error accumulation for final results. In this work, we make the first attempt to explore a detection-free Part Grouping Network (PGN) for efficiently parsing multiple people in an image in a single pass. Our PGN reformulates instance-level human parsing as two twinned sub-tasks that can be jointly learned and mutually refined via a unified network: (1) semantic part segmentation for assigning each pixel as a human part (e.g., face, arms); (2) instance-aware edge detection to group semantic parts into distinct person instances. Thus the shared intermediate representation would be endowed with capabilities in both characterizing fine-grained parts and inferring instance belongings of each part. Finally, a simple instance partition process is employed to get final results during inference. We conducted experiments on PASCAL-Person-Part dataset and our PGN outperforms all state-of-the-art methods. Furthermore, we show its superiority on a newly collected multi-person parsing dataset (CIHP) including 38,280 diverse images, which is the largest dataset so far and can facilitate more advanced human analysis. The CIHP benchmark and our source code are available at http: sysu-hcp.net lip . In this work, we address the problem of human parsing, namely partitioning the human body into semantic regions, by using the novel Parselet representation. Previous works often consider solving the problem of human pose estimation as the prerequisite of human parsing. We argue that these approaches cannot obtain optimal pixel level parsing due to the inconsistent targets between these tasks. In this paper, we propose to use Parselets as the building blocks of our parsing model. Parselets are a group of parsable segments which can generally be obtained by low-level over-segmentation algorithms and bear strong semantic meaning. We then build a Deformable Mixture Parsing Model (DMPM) for human parsing to simultaneously handle the deformation and multi-modalities of Parselets. The proposed model has two unique characteristics: (1) the possible numerous modalities of Parse let ensembles are exhibited as the And-Or" structure of sub-trees, (2) to further solve the practical problem of Parselet occlusion or absence, we directly model the visibility property at some leaf nodes. The DMPM thus directly solves the problem of human parsing by searching for the best graph configuration from a pool of Parse let hypotheses without intermediate tasks. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach. Human parsing has recently attracted a lot of research interests due to its huge application potentials. However existing datasets have limited number of images and annotations, and lack the variety of human appearances and the coverage of challenging cases in unconstrained environment. In this paper, we introduce a new benchmark Look into Person (LIP) that makes a significant advance in terms of scalability, diversity and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels, which are captured from a wider range of viewpoints, occlusions and background complexity. Given these rich annotations we perform detailed analysis of the leading human parsing approaches, gaining insights into the success and failures of these methods. Furthermore, in contrast to the existing efforts on improving the feature discriminative capability, we solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into parsing results without resorting to extra supervision (i.e., no need for specifically labeling human joints in model training). Our self-supervised learning framework can be injected into any advanced neural networks to help incorporate rich high-level knowledge regarding human joints from a global perspective and improve the parsing results. Extensive evaluations on our LIP and the public PASCAL-Person-Part dataset demonstrate the superiority of our method.