论文标题
Animgan：一种用于角色动画的时空条件生成对抗网络
AnimGAN: A Spatiotemporally-Conditioned Generative Adversarial Network for Character Animation
论文作者
论文摘要
制作现实的角色动画是人类互动中的重要任务之一。该任务被认为是人形生物的一系列姿势，可以将其视为具有时空平滑性和现实主义约束的序列产生问题。此外，我们希望通过给予他们做什么，更具体地说，是如何做的，以控制AI代理的行为。我们提出了一种空间条件的gan，该gan产生了与给定序列相似的序列，就语义和时空动力学而言。使用基于LSTM的生成器和图形Convnet鉴别器，该系统在大型收集的手势，表达式和动作的数据集上进行了训练。实验表明，与传统的有条件GAN相比，我们的方法创建了与用户期望相匹配的合理，现实和语义相关的类人动物动画序列。
Producing realistic character animations is one of the essential tasks in human-AI interactions. Considered as a sequence of poses of a humanoid, the task can be considered as a sequence generation problem with spatiotemporal smoothness and realism constraints. Additionally, we wish to control the behavior of AI agents by giving them what to do and, more specifically, how to do it. We proposed a spatiotemporally-conditioned GAN that generates a sequence that is similar to a given sequence in terms of semantics and spatiotemporal dynamics. Using LSTM-based generator and graph ConvNet discriminator, this system is trained end-to-end on a large gathered dataset of gestures, expressions, and actions. Experiments showed that compared to traditional conditional GAN, our method creates plausible, realistic, and semantically relevant humanoid animation sequences that match user expectations.
