用于运动风格化的D-LORD
D-LORD for Motion Stylization
AI总结：
本文提出D-LORD框架，通过双潜在优化解耦运动序列的类别（风格）与内容信息，无需配对数据即可实现运动风格迁移与重定向，在三个数据集验证了有效性，是首个通用框架。
摘要：
本文介绍了一个名为D-LORD（表示解耦的双潜在优化，Double Latent Optimization for Representation Disentanglement）的新型框架，旨在实现运动风格化（运动风格迁移与运动重定向）。该框架的主要目标是通过数据驱动的潜在优化方法，从给定的运动序列中分离类别和内容信息。其中，类别指特定于人的风格，如特定情绪或个体身份；内容则与动作的风格无关方面相关，如行走或跳跃等普遍理解的概念。D-LORD的关键优势在于无需配对运动数据即可进行风格迁移，而是在潜在优化过程中利用类别和内容标签。通过解耦表示，该框架借助自适应实例归一化（Adaptive Instance Normalization）实现将一个运动序列的风格转换为另一个的风格。所提出的D-LORD框架注重泛化能力，能够处理不同应用场景的各类别和内容标签；此外，当提供特定类别和内容标签时，它可生成多样化的运动序列。该框架的有效性通过三个数据集的实验得到验证：用于运动风格迁移的CMU XIA数据集，以及用于运动重定向的MHAD数据集和RRIS Ability数据集。值得注意的是，本文提出了首个用于运动风格迁移和运动重定向的通用框架，展示了其在该领域的潜在贡献。
英文摘要：
This paper introduces a novel framework named D-LORD (Double Latent Optimization for Representation Disentanglement), 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 sequences 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. Additionally, 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: the CMU XIA dataset for motion style transfer, the MHAD dataset, and the RRIS Ability dataset for motion retargeting. Notably, this paper presents the first generalized framework for motion style transfer and motion retargeting, showcasing its potential contributions in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
