In recent years, emotional text-to-speech has shown considerable progress.
However, it requires a large amount of labeled data, which is not easily
accessible. Even if it is possible to acquire an emotional speech dataset,
there is still a limitation in controlling emotion intensity. In this work, we
propose a novel method for cross-speaker emotion transfer and manipulation
using vector arithmetic in latent style space. By leveraging only a few labeled
samples, we generate emotional speech from reading-style speech without losing
the speaker identity. Furthermore, emotion strength is readily controllable
using a scalar value, providing an intuitive way for users to manipulate
speech. Experimental results show the proposed method affords superior
performance in terms of expressiveness, naturalness, and controllability,
preserving speaker identity.

本文提出一种基于潜在风格空间中的矢量算术方法的跨说话人情感转移和操纵研究，可以使用仅有的几个标记样本从阅读风格语音生成情感语音，并且情感强度可以轻松控制，保留了说话人的身份。实验结果表明这种方法在表达性、自然度和可控性方面具有优越性。

通过操作语音风格潜在因素进行跨说话人情感转移

Cross-speaker Emotion Transfer by Manipulating Speech Style Latents

Image-to-Image (I2I) multi-domain translation models are usually evaluated
also using the quality of their semantic interpolation results. However,
state-of-the-art models frequently show abrupt changes in the image appearance
during interpolation, and usually perform poorly in interpolations across
domains. In this paper, we propose a new training protocol based on three
specific losses which help a translation network to learn a smooth and
disentangled latent style space in which: 1) Both intra- and inter-domain
interpolations correspond to gradual changes in the generated images and 2) The
content of the source image is better preserved during the translation.
Moreover, we propose a novel evaluation metric to properly measure the
smoothness of latent style space of I2I translation models. The proposed method
can be plugged into existing translation approaches, and our extensive
experiments on different datasets show that it can significantly boost the
quality of the generated images and the graduality of the interpolations.

本文提出了一种基于三个特定损失的新训练协议，并提出了一种新的评估度量来正确衡量 I2I 翻译模型的潜在风格空间的平滑性，实验结果表明该方法可以显著提高生成图像的质量和插值的渐进性。

无监督图像转换中平滑解缕隐空间

Smoothing the Disentangled Latent Style Space for Unsupervised  Image-to-Image Translation

We explore and analyze the latent style space of StyleGAN2, a
state-of-the-art architecture for image generation, using models pretrained on
several different datasets. We first show that StyleSpace, the space of
channel-wise style parameters, is significantly more disentangled than the
other intermediate latent spaces explored by previous works. Next, we describe
a method for discovering a large collection of style channels, each of which is
shown to control a distinct visual attribute in a highly localized and
disentangled manner. Third, we propose a simple method for identifying style
channels that control a specific attribute, using a pretrained classifier or a
small number of example images. Manipulation of visual attributes via these
StyleSpace controls is shown to be better disentangled than via those proposed
in previous works. To show this, we make use of a newly proposed Attribute
Dependency metric. Finally, we demonstrate the applicability of StyleSpace
controls to the manipulation of real images. Our findings pave the way to
semantically meaningful and well-disentangled image manipulations via simple
and intuitive interfaces.

通过对预先训练在多个数据集上的模型进行分析，本文探讨和分析了 StyleGAN2 的潜在样式空间，发现 StyleSpace 的通道式样式参数空间显著比前人的其他中间潜变空间更具解开性，提出了一种通过 StyleSpace 控制实现更好的视觉属性解开性的方法，最终展示了应用 StyleSpace 控制实现对真实图像的操作，为通过简单而直观的接口实现具有语义意义的、良好解开的图像操作铺平了道路。