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Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency
Fang Zhao · Shengcai Liao · Kaihao Zhang · Ling Shao

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #397

This paper proposes a human parsing based texture transfer model via cross-view consistency learning to generate the texture of 3D human body from a single image. We use the semantic parsing of human body as input for providing both the shape and pose information to reduce the appearance variation of human image and preserve the spatial distribution of semantic parts. Meanwhile, in order to improve the prediction for textures of invisible parts, we explicitly enforce the consistency across different views of the same subject by exchanging the textures predicted by two views to render images during training. The perceptual loss and total variation regularization are optimized to maximize the similarity between rendered and input images, which does not necessitate extra 3D texture supervision. Experimental results on pedestrian images and fashion photos demonstrate that our method can produce higher quality textures with convincing details than other texture generation methods.

Author Information

Fang Zhao (Inception Institute of Artificial Intelligence)
Shengcai Liao (Inception Institute of Artificial Intelligence)

Shengcai Liao is a Lead Scientist in the Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, UAE. He is a Senior Member of IEEE. Previously, he was an Associate Professor in the Institute of Automation, Chinese Academy of Sciences (CASIA). He received the B.S. degree in mathematics from the Sun Yat-sen University in 2005 and the Ph.D. degree from CASIA in 2010. He was a Postdoc in the Michigan State University during 2010-2012. His research interests include object detection, recognition, and tracking, especially face and person related tasks. He has published over 100 papers, with **over 14,900 citations and h-index 43** according to Google Scholar. He **ranks 905 among 215,114 scientists (Top 0.42%)** in 2019 single year in the field of AI, according to a study by Stanford University of Top 2% world-wide scientists. His representative work LOMO+XQDA, known for effective feature design and metric learning for person re-identification, has been **cited over 1,900 times and ranks top 10 among 602 papers in CVPR 2015**. He was awarded the Best Student Paper in ICB 2006, ICB 2015, and CCBR 2016, and the Best Paper in ICB 2007. He was also awarded the IJCB 2014 Best Reviewer and CVPR 2019/2021 Outstanding Reviewer. He was an Assistant Editor for the book “Encyclopedia of Biometrics (2nd Ed.)”. He will serve as Program Chair for IJCB 2022, and Area Chair for CVPR 2022 and ECCV 2022. He served as Area Chairs for ICPR 2016, ICB 2016 and 2018, SPC for IJCAI 2021, and reviewers for ICCV, CVPR, ECCV, NeurIPS, ICLR, AAAI, TPAMI, IJCV, TNNLS, etc. He was the Winner of the CVPR 2017 Detection in Crowded Scenes Challenge and ICCV 2019 NightOwls Pedestrian Detection Challenge.

Kaihao Zhang (Australian National University)
Ling Shao (Inception Institute of Artificial Intelligence)

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