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Human parsing and pose estimation have recently received considerableinterest due to their substantial application potentials. However, the existingdatasets have limited numbers of images and annotations and lack a variety ofhuman appearances and coverage of challenging cases in unconstrainedenvironments. In this paper, we introduce a new benchmark named "Look intoPerson (LIP)" that provides a significant advancement in terms of scalability,diversity, and difficulty, which are crucial for future developments inhuman-centric analysis. This comprehensive dataset contains over 50,000elaborately annotated images with 19 semantic part labels and 16 body joints,which are captured from a broad range of viewpoints, occlusions, and backgroundcomplexities. Using these rich annotations, we perform detailed analyses of theleading human parsing and pose estimation approaches, thereby obtaininginsights into the successes and failures of these methods. To further exploreand take advantage of the semantic correlation of these two tasks, we propose anovel joint human parsing and pose estimation network to explore efficientcontext modeling, which can simultaneously predict parsing and pose withextremely high quality. Furthermore, we simplify the network to solve humanparsing by exploring a novel self-supervised structure-sensitive learningapproach, which imposes human pose structures into the parsing results withoutresorting to extra supervision. The dataset, code and models are available athttp://www.sysu-hcp.net/lip/.

Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark