CASIA OpenIR  > 智能感知与计算
Order-aware Human Interaction Manipulation
Luo, Mandi1,2; Cao, Jie1,2; He, Ran1,2
2022-10
Conference NameACM International Conference on Multimedia
Conference Date2022.10.10-2022.10.14
Conference PlaceLisboa, Portugal
Abstract

The majority of current techniques for pose transfer disregard the interactions between the transferred person and the surrounding instances, resulting in context inconsistency when applied to complicated situations. To tackle this issue, we propose InterOrderNet, a novel framework to perform order-aware interaction learning. The proposed InterOrderNet learns the relative order on the direction of the z-axis among instances to describe instance-level occlusions. Not only does learning this order guarantee the context consistency of human pose transfer, but it also enhances its generalization to natural scenes. Additionally, we present a novel unsupervised method, named Imitative Contrastive Learning, which sidesteps the requirements of order annotations. Existing pose transfer methods are easy to be integrated into the proposed InterOrderNet. Extensive experiments demonstrate that InterOrderNet enables these methods to perform interaction manipulation.

Indexed ByEI
IS Representative Paper
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49667
Collection智能感知与计算
Corresponding AuthorHe, Ran
Affiliation1.School of Artificial Intelligence, UCAS
2.NLPR, CAS
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Luo, Mandi,Cao, Jie,He, Ran. Order-aware Human Interaction Manipulation[C],2022.
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