Facial expression retargeting from humans to virtual characters is a useful
technique in computer graphics and animation. Traditional methods use markers
or blendshapes to construct a mapping between the human and avatar faces.
However, these approaches require a tedious 3D modeling process, and the
performance relies on the modelers' experience. In this paper, we propose a
brand-new solution to this cross-domain expression transfer problem via
nonlinear expression embedding and expression domain translation. We first
build low-dimensional latent spaces for the human and avatar facial expressions
with variational autoencoder. Then we construct correspondences between the two
latent spaces guided by geometric and perceptual constraints. Specifically, we
design geometric correspondences to reflect geometric matching and utilize a
triplet data structure to express users' perceptual preference of avatar
expressions. A user-friendly method is proposed to automatically generate
triplets for a system allowing users to easily and efficiently annotate the
correspondences. Using both geometric and perceptual correspondences, we
trained a network for expression domain translation from human to avatar.
Extensive experimental results and user studies demonstrate that even
nonprofessional users can apply our method to generate high-quality facial
expression retargeting results with less time and effort.

本文提出了一种基于非线性表情嵌入和表情域翻译的跨域表情转移技术，通过低维潜空间、几何约束、感知约束和用户友好的技术，实现了人脸表情转移到虚拟角色上，并且在大范围实验和用户研究中证明了该方法的高效性和高质量。

从人类到模型角色的面部表情再现简化

Facial Expression Retargeting from Human to Avatar Made Easy

We consider the problem of image-to-video translation, where an input image
is translated into an output video containing motions of a single object.
Recent methods for such problems typically train transformation networks to
generate future frames conditioned on the structure sequence. Parallel work has
shown that short high-quality motions can be generated by spatiotemporal
generative networks that leverage temporal knowledge from the training data. We
combine the benefits of both approaches and propose a two-stage generation
framework where videos are generated from structures and then refined by
temporal signals. To model motions more efficiently, we train networks to learn
residual motion between the current and future frames, which avoids learning
motion-irrelevant details. We conduct extensive experiments on two
image-to-video translation tasks: facial expression retargeting and human pose
forecasting. Superior results over the state-of-the-art methods on both tasks
demonstrate the effectiveness of our approach.

本文提出了一个两阶段的生成框架来解决图像到视频转换的问题，其中视频从结构生成并通过时间信号进行调整。通过在面部表情重定位和人体姿势预测这两个任务中取得比现有方法优秀的结果，证明了我们方法的有效性。