In-betweening is a technique for generating transitions given initial and
target character states. The majority of existing works require multiple (often
$>$10) frames as input, which are not always accessible. Our work deals with a
focused yet challenging problem: to generate the transition when given exactly
two frames (only the first and last). To cope with this challenging scenario,
we implement our bi-directional scheme which generates forward and backward
transitions from the start and end frames with two adversarial autoregressive
networks, and stitches them in the middle of the transition where there is no
strict ground truth. The autoregressive networks based on conditional
variational autoencoders (CVAE) are optimized by searching for a pair of
optimal latent codes that minimize a novel stitching loss between their
outputs. Results show that our method achieves higher motion quality and more
diverse results than existing methods on both the LaFAN1 and Human3.6m
datasets.

使用基于条件变分自动编码器的自回归网络实现单两帧生成过渡，结果表明其在动作质量和多样性方面均比现有方法更加优秀。

双姿态拼接的多样化动画中间帧生成

Diverse Motion In-betweening with Dual Posture Stitching

Motion completion is a challenging and long-discussed problem, which is of
great significance in film and game applications. For different motion
completion scenarios (in-betweening, in-filling, and blending), most previous
methods deal with the completion problems with case-by-case designs. In this
work, we propose a simple but effective method to solve multiple motion
completion problems under a unified framework and achieves a new state of the
art accuracy under multiple evaluation settings. Inspired by the recent great
success of attention-based models, we consider the completion as a sequence to
sequence prediction problem. Our method consists of two modules - a standard
transformer encoder with self-attention that learns long-range dependencies of
input motions, and a trainable mixture embedding module that models temporal
information and discriminates key-frames. Our method can run in a
non-autoregressive manner and predict multiple missing frames within a single
forward propagation in real time. We finally show the effectiveness of our
method in music-dance applications.

本文提出了一种有效的方法来解决多种动作完成问题，在多个评估设置下达到新的艺术状态准确性。该方法由两个模块组成，标准 Transformer 编码器和可训练混合嵌入模块，可以在实时单向传播中预测多个缺失帧。

基于 Transformer 的单次运动补全

Single-Shot Motion Completion with Transformer

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。