IEEE Trans Vis Comput Graph. 2021 Jun;27(6):2967-2979. doi: 10.1109/TVCG.2019.2953063. Epub 2021 May 12.
We present a data-driven algorithm for generating gaits of virtual characters with varying dominance traits. Our formulation utilizes a user study to establish a data-driven dominance mapping between gaits and dominance labels. We use our dominance mapping to generate walking gaits for virtual characters that exhibit a variety of dominance traits while interacting with the user. Furthermore, we extract gait features based on known criteria in visual perception and psychology literature that can be used to identify the dominance levels of any walking gait. We validate our mapping and the perceived dominance traits by a second user study in an immersive virtual environment. Our gait dominance classification algorithm can classify the dominance traits of gaits with ˜73 percent accuracy. We also present an application of our approach that simulates interpersonal relationships between virtual characters. To the best of our knowledge, ours is the first practical approach to classifying gait dominance and generate dominance traits in virtual characters.
我们提出了一种数据驱动的算法，用于生成具有不同主导特质的虚拟角色的步态。我们的公式利用用户研究在步态和主导标签之间建立了一种数据驱动的主导映射。我们使用主导映射为虚拟角色生成行走步态，这些步态在与用户交互时表现出各种主导特质。此外，我们根据视觉感知和心理学文献中的已知标准提取步态特征，这些特征可用于识别任何行走步态的主导水平。我们通过在沉浸式虚拟环境中的第二个用户研究验证了我们的映射和感知的主导特质。我们的步态主导分类算法可以以约 73%的准确率对步态的主导特质进行分类。我们还展示了我们方法的一个应用，该应用模拟了虚拟角色之间的人际关系。据我们所知，我们的方法是第一种用于对虚拟角色的步态主导进行分类和生成主导特质的实用方法。
