While existing motion style transfer methods are effective between two
motions with identical content, their performance significantly diminishes when
transferring style between motions with different contents. This challenge lies
in the lack of clear separation between content and style of a motion. To
tackle this challenge, we propose a novel motion style transformer that
effectively disentangles style from content and generates a plausible motion
with transferred style from a source motion. Our distinctive approach to
achieving the goal of disentanglement is twofold: (1) a new architecture for
motion style transformer with 'part-attentive style modulator across body
parts' and 'Siamese encoders that encode style and content features
separately'; (2) style disentanglement loss. Our method outperforms existing
methods and demonstrates exceptionally high quality, particularly in motion
pairs with different contents, without the need for heuristic post-processing.
Codes are available at this https URL

基于新的模型架构和风格解耦损失，我们提出了一种有效解决在不同内容的运动之间进行风格传递的问题的运动风格转换器，它能够将风格与内容有效分离，并生成具有传入风格的逼真运动。我们的方法在各方面表现优于现有方法，在不同内容的运动对中表现出特别高的质量，而无需启发式后处理。

MoST：多种动作内容之间的动作风格转换网络

MoST: Motion Style Transformer between Diverse Action Contents

In this paper, we tackle the unsupervised domain adaptation (UDA) for
semantic segmentation, which aims to segment the unlabeled real data using
labeled synthetic data. The main problem of UDA for semantic segmentation
relies on reducing the domain gap between the real image and synthetic image.
To solve this problem, we focused on separating information in an image into
content and style. Here, only the content has cues for semantic segmentation,
and the style makes the domain gap. Thus, precise separation of content and
style in an image leads to effect as supervision of real data even when
learning with synthetic data. To make the best of this effect, we propose a
zero-style loss. Even though we perfectly extract content for semantic
segmentation in the real domain, another main challenge, the class imbalance
problem, still exists in UDA for semantic segmentation. We address this problem
by transferring the contents of tail classes from synthetic to real domain.
Experimental results show that the proposed method achieves the
state-of-the-art performance in semantic segmentation on the major two UDA
settings.

本研究提出了一种基于内容和风格分离的零风格损失的无监督域自适应方法，通过将合成数据的标注用于真实数据的分割，有效地解决了语义分割中的领域间差异和类别不平衡问题。

无监督领域适应学习用于语义分割的内容迁移技术

Unsupervised Domain Adaptation for Semantic Segmentation by Content  Transfer

In fine art, especially painting, humans have mastered the skill to create
unique visual experiences through composing a complex interplay between the
content and style of an image. Thus far the algorithmic basis of this process
is unknown and there exists no artificial system with similar capabilities.
However, in other key areas of visual perception such as object and face
recognition near-human performance was recently demonstrated by a class of
biologically inspired vision models called Deep Neural Networks. Here we
introduce an artificial system based on a Deep Neural Network that creates
artistic images of high perceptual quality. The system uses neural
representations to separate and recombine content and style of arbitrary
images, providing a neural algorithm for the creation of artistic images.
Moreover, in light of the striking similarities between performance-optimised
artificial neural networks and biological vision, our work offers a path
forward to an algorithmic understanding of how humans create and perceive
artistic imagery.

通过使用深度神经网络对任意图像的内容和风格进行分离和重组，我们引入了一种人工系统，为艺术形象的创作提供了神经算法，并且在视觉感知的其他关键领域中的近乎人类性能展示了深度神经网络模型的类生物启发式视觉模型的特定任务的卓越表现的显着相似之处，为人类创作和感知艺术意象的算法理解提供了一条前进的路径。