Generating realistic talking faces is an interesting and long-standing topic
in the field of computer vision. Although significant progress has been made,
it is still challenging to generate high-quality dynamic faces with
personalized details. This is mainly due to the inability of the general model
to represent personalized details and the generalization problem to unseen
controllable parameters. In this work, we propose Myportrait, a simple,
general, and flexible framework for neural portrait generation. We incorporate
personalized prior in a monocular video and morphable prior in 3D face
morphable space for generating personalized details under novel controllable
parameters. Our proposed framework supports both video-driven and audio-driven
face animation given a monocular video of a single person. Distinguished by
whether the test data is sent to training or not, our method provides a
real-time online version and a high-quality offline version. Comprehensive
experiments in various metrics demonstrate the superior performance of our
method over the state-of-the-art methods. The code will be publicly available.

在计算机视觉领域，生成逼真的说话脸部是一个有趣且长期存在的课题。尽管已经取得了显著的进展，但是生成具有个性化细节的高质量动态脸部仍然具有挑战性。本文提出了一种简单、通用且灵活的神经画廊生成框架 Myportrait，在单目视频中加入个性化先验和三维人脸形变空间的可变先验，在新的可控参数下生成个性化细节。我们的框架支持基于视频和基于音频的面部动画，给定单个人的单目视频。通过测试数据是否发送到训练中，我们的方法提供了实时在线版本和高质量离线版本。广泛的实验证明了我们方法在各个指标上优于最先进方法。代码将公开发布。

MyPortrait: 可塑形先导定制肖像生成

MyPortrait: Morphable Prior-Guided Personalized Portrait Generation

We address the problem of estimating human pose and body shape from 3D scans
over time. Reliable estimation of 3D body shape is necessary for many
applications including virtual try-on, health monitoring, and avatar creation
for virtual reality. Scanning bodies in minimal clothing, however, presents a
practical barrier to these applications. We address this problem by estimating
body shape under clothing from a sequence of 3D scans. Previous methods that
have exploited body models produce smooth shapes lacking personalized details.
We contribute a new approach to recover a personalized shape of the person. The
estimated shape deviates from a parametric model to fit the 3D scans. We
demonstrate the method using high quality 4D data as well as sequences of
visual hulls extracted from multi-view images. We also make available BUFF, a
new 4D dataset that enables quantitative evaluation
(this http URL). Our method outperforms the state of the art in
both pose estimation and shape estimation, qualitatively and quantitatively.

本研究提出了一种新方法，可以从穿着衣服的 3D 扫描序列中恢复个人化的人体形状，其性能在姿态估计和形状估计方面均优于现有技术。