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.

本文提出了一种基于对抗性递归神经网络的新型、稳健的过渡生成技术，可以作为 3D 动画师的新工具。系统合成了高质量的运动，使用时间稀疏的关键帧作为动画约束。我们提出了两种新的加性嵌入修饰符，它们被应用在网络架构中的潜在表示内，用于解决仅在未来关键帧添加调节信息时无法将状态 - of-the-art 的运动预测模型转换为稳健的过渡生成器的问题。为了定量评估我们的方法，我们在 Human3.6M 数据集的子集和适用于转换生成的新型佐佛摸捉数据集 LaFAN1 上提供了明确定义的中间演算基准。同时，本文还发布了这个新数据集和可重现我们基准结果的伴随代码。