In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesizes high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.
翻译:在此工作中,我们展示了一种新型的、稳健的过渡生成技术,这种技术可以作为3D动画家的新工具,其基础是对抗性反复的神经网络。这个系统综合了高质量的运动动作,这些动作使用暂时扭曲的键框架作为动画限制。这是传统动画管道中间工作的一种记忆,其中动画家在提供的关键框架之间绘制运动框架。我们首先显示,在仅仅添加关于未来关键框架的固定信息时,一个最先进的运动预测模型不能轻易地转换成一个强大的过渡生成器。为了解决这个问题,我们然后提出两个新颖的添加剂,在网络架构内对潜在显示进行编码的每个时间步骤都应用这些高品质的修改器。一个修改器是一个时间到到达时间的嵌入式,允许使用单一模型来改变过渡时间长度。另一个是预定的目标噪声矢量矢量矢量矢量矢量,使系统能够稳健地瞄准扭曲目标,并且根据固定的关键框架对不同的转变进行抽样。为了从质量上评估我们的方法,我们提出了一个定制的移动器插插插插插插插插插插插件,用我们这个经过这个经过长期的转换模型,在生产模型中,用我们这个经过时间模型,用来在生产模型中进行更精确的模型中,在制作模型中,在生产模型中,用一个更精确的模模模模模模模模模模模模模模模版的数据模模模模模模模模。