Congratulation to Peike, Yunqiu on their winning in three tracks in LIP.
posted on June 11, 2019


Among the 75 participating teams in the world, our team is proud to announce that we have won the Look Into Person (LIP) Challenge for three tasks, including the single-person human parsing, the multi-person human parsing and multi-person video parsing tracks. LIP challenge has attracted many world-famous AI companies and universities including the ByteDance AI Lab, JD AI Lab, Samsung AI Lab UCLA, Cambridge and so on.

LIP Challenge is co-organized with workshop on augmented human: human-centric understanding. The workshop is co-located with CVPR 2019, the most important annual computer vision conference, to be held in Long Beach, California 16-23 June. The goal of this workshop is to allow researchers from the fields of human-centric understanding and 2D/3D synthesis to present their progress, communication and co-develop novel ideas that potentially shape the future of this area and further advance the performance and applicability of correspondingly built systems in real-world conditions.

The results can be found in https://vuhcs.github.io/.

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The third large-scale Look Into Person (LIP) challenges which include five competition tasks: the single-person human parsing, the single-person pose estimation, the multi-person human parsing, multi-person video parsing, multi-person pose estimation benchmark, and clothes virtual try-on benchmark. This third LIP challenge mainly extends the second LIP challenge in CVPR 2017 and CVPR 2018 by additionally covering a video human parsing challenge and the 2D/3D clothes virtual try-on benchmark. For the single-person human parsing and pose estimation, 50,000 images with elaborated pixel-wise annotations with comprehensive 19 semantic human part labels and 2D human poses with 16 dense key points are provided. For the multi-person human parsing competition task, another 50000 images of crowded scenes with 19 semantic human part labels are provided. For video-based human parsing, 3000 video shots with 1-2 minutes will be densely annotated with 19 semantic human part labels. For multi-person pose estimation, the dataset contains 25,828 images with 2D human poses with 16 dense key points and head & instance bounding boxes. The new image-based clothes try-on benchmark targets at fitting new in-shop clothes into a person image and generate one try-on video to show different clothes viewpoints on the person. The images collected from the real-world scenarios contain humans appearing with challenging poses and views, heavily occlusions, various appearances and low-resolutions. This challenge was released before January, 2019 and the challenge is conjunction with CVPR 2019, Long Beach, CA.

Single Person Human Parsing Task

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Multi-Person Human Parsing Task

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Video Multi-Person Human Parsing Task

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Human Parsing is the sub-task of semantic segmentation which need to assign a semantic label (like leg or arm) to each pixel in a human image. We proposed a novel SPHP(Single Person Human Parsing) network, which consists of three key modules to learn for parsing in an end-to-end manner including high resolution embedding module, global context embedding module and edge perceiving module.

Framework of our winning solution

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Some Qualitative Results of Our Single Person Human Parsing Solution

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Some Qualitative Results of Our Multi-Person Human Parsing Solution

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Some Qualitative Results of Our Video Multi-Person Human Parsing Solution

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The detailed report and code will be released later.