Order-aware Human Interaction Manipulation | |
Luo, Mandi1,2; Cao, Jie1,2; He, Ran1,2 | |
2022-10 | |
会议名称 | ACM International Conference on Multimedia |
会议日期 | 2022.10.10-2022.10.14 |
会议地点 | Lisboa, Portugal |
摘要 | 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. |
收录类别 | EI |
是否为代表性论文 | 是 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49667 |
专题 | 智能感知与计算研究中心 |
通讯作者 | He, Ran |
作者单位 | 1.School of Artificial Intelligence, UCAS 2.NLPR, CAS |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Luo, Mandi,Cao, Jie,He, Ran. Order-aware Human Interaction Manipulation[C],2022. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Order_aware_interact(3872KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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