.png){kind=icon}
返回
D-LORD for Motion Stylization
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2024.3502498.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article introduces a novel framework named double-latent optimization for representation disentanglement (D-LORD), which is designed for motion stylization (motion style transfer and motion retargeting). The primary objective of this framework is to separate the class and content information from a given motion sequence using a data-driven latent optimization approach. Here, class refers to person-specific style, such as a particular emotion or an individual's identity, while content relates to the style-agnostic aspect of an action, such as walking or jumping, as universally understood concepts. The key advantage of D-LORD is its ability to perform style transfer without needing paired motion data. Instead, it utilizes class and content labels during the latent optimization process. By disentangling the representation, the framework enables the transformation of one motion sequence's style to another's style using adaptive instance normalization. The proposed D-LORD framework is designed with a focus on generalization, allowing it to handle different class and content labels for various applications. In addition, it can generate diverse motion sequences when specific class and content labels are provided. The framework's efficacy is demonstrated through experimentation on three datasets: 1) the CMU XIA dataset for motion style transfer; 2) the multimodal human action database dataset; and 3) the RRIS Ability dataset for motion retargeting. Notably, this article presents the first generalized framework for motion style transfer and motion retargeting, showcasing its potential contributions in this area.
Keyword:
Double latent optimization for representation disentanglement
motion retargeting
motion style transfer
Double latent optimization for representation disentanglement
motion retargeting
motion style transfer
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
ARVino - outdoor augmented reality visualisation of viticulture GIS dataARVino-葡萄栽培GIS数据的户外增强现实可视化
OAAI
.png){kind=icon}
OAAI分享收藏
Surgical therapy and next-generation sequencing-based genetic alteration analysis of malignant solitary fibrous tumor of the pleura胸膜恶性孤立性纤维瘤的外科治疗和基于二代测序的遗传学改变分析OAAI
OAAI分享收藏The Effect of 40-Hz Light Therapy on Amyloid Load in Patients with Prodromal and Clinical Alzheimer’s Disease40Hz光疗法对前驱和临床阿尔茨海默病患者淀粉样蛋白负荷的影响OAAI
OAAI分享收藏
查看更多内容