{"type":"ScholarlyArticle","title":"StackFLOW: Monocular Human-Object Reconstruction by Stacked Normalizing Flow with Offset","authors":[{"name":"Chaofan Huo","slug":"chaofan-huo","given":"Chaofan","family":"Huo","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"},{"name":"Ye Shi","slug":"ye-shi","given":"Ye","family":"Shi","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"},{"name":"Yuexin Ma","slug":"yuexin-ma","given":"Yuexin","family":"Ma","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"},{"name":"Lan Xu","slug":"lan-xu","given":"Lan","family":"Xu","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"},{"name":"Jingyi Yu","slug":"jingyi-yu","given":"Jingyi","family":"Yu","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"},{"name":"Jingya Wang","slug":"jingya-wang","given":"Jingya","family":"Wang","affiliation":"ShanghaiTech University; Shanghai Engineering Research Center of Intelligent Vision and Imaging {huochf , shiye ,"}],"abstract":"Modeling and capturing the 3D spatial arrangement of the human and the object is the key to perceiving 3D human-object interaction from monocular images. In this work, we propose to use the Human-Object Offset between anchors which are densely sampled from the surface of human mesh and object mesh to represent human-object spatial relation. Compared with previous works which use contact map or implicit distance filed to encode 3D human-object spatial relations, our method is a simple and efficient way to encode the highly detailed spatial correlation between the human and object. Based on this representation, we propose Stacked Normalizing Flow (StackFLOW) to infer the posterior distribution of human-object spatial relations from the image. During the optimization stage, we finetune the human body pose and object 6D pose by maximizing the likelihood of samples based on this posterior distribution and minimizing the 2D-3D corresponding reprojection loss. Extensive experimental results show that our method achieves impressive results on two challenging benchmarks, BEHAVE and InterCap datasets. Our code has been publicly available at https://github.com/MoChen-bop/StackFLOW.","doi":"10.24963/ijcai.2023/100","url":"https://ijcai.org/proceedings/2023/100","pdf":"https://ijcai.org/proceedings/2023/0100.pdf","markdown":"https://ijcai.org/proceedings/2023/100.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/100","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":100,"pages":{"first":902,"last":910},"keywords":["Computer Vision: CV: 3D computer vision","Computer Vision: CV: Biometrics, face, gesture and pose recognition","Computer Vision: CV: Action and behavior recognition"],"videos":[],"references":[{"position":1,"raw":"[Aliakbarian et al., 2022] Sadegh Aliakbarian, Pashmina Cameron, Federica Bogo, Andrew Fitzgibbon, and Thomas J. 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