论文标题
三角形角色动画采样，情感和关系
Triangular Character Animation Sampling with Motion, Emotion, and Relation
论文作者
论文摘要
在使个体角色动画中取得了巨大的进步。但是，我们仍然缺乏对角色之间的活动的自动控制，尤其是涉及互动的活动。在本文中，我们提出了一个新颖的基于能量的框架，可以通过关联角色的身体动作，面部表情和社会关系来取样和综合动画。我们提出了一个空间和-OR图（ST-AOG），一种随机语法模型，以编码运动，情感和关系之间的上下文关系，在条件随机场中形成三角形。我们从标记的两个字符交互数据集中训练模型。实验表明，我们的方法可以使用马尔可夫链蒙特卡洛（MCMC）认识到两个字符和样本生动运动和情感的新场景之间的社会关系。因此，我们的方法可以为动画师提供一种自动生成3D字符动画的方法，帮助合成非播放器字符（NPC）之间的互动，并在虚拟现实（VR）中增强机器情感智能（EQ）。
Dramatic progress has been made in animating individual characters. However, we still lack automatic control over activities between characters, especially those involving interactions. In this paper, we present a novel energy-based framework to sample and synthesize animations by associating the characters' body motions, facial expressions, and social relations. We propose a Spatial-Temporal And-Or graph (ST-AOG), a stochastic grammar model, to encode the contextual relationship between motion, emotion, and relation, forming a triangle in a conditional random field. We train our model from a labeled dataset of two-character interactions. Experiments demonstrate that our method can recognize the social relation between two characters and sample new scenes of vivid motion and emotion using Markov Chain Monte Carlo (MCMC) given the social relation. Thus, our method can provide animators with an automatic way to generate 3D character animations, help synthesize interactions between Non-Player Characters (NPCs), and enhance machine emotion intelligence (EQ) in virtual reality (VR).
