Previous work on controllable text generation has explored the idea of
control from the latent space, such as optimizing a representation with
attribute-related classifiers or sampling a representation from relevant
discrete samples. However, they are not effective enough in modeling both the
latent space and the control, leaving controlled text with low quality and
diversity. In this work, we propose a novel control framework using probability
density estimation in the latent space. Our method utilizes an invertible
transformation function, the Normalizing Flow, that maps the complex
distributions in the latent space to simple Gaussian distributions in the prior
space. Thus, we can perform sophisticated and flexible control in the prior
space and feed the control effects back into the latent space owing to the
one-one-mapping property of invertible transformations. Experiments on
single-attribute controls and multi-attribute control reveal that our method
outperforms several strong baselines on attribute relevance and text quality
and achieves the SOTA. Further analysis of control strength adjustment
demonstrates the flexibility of our control strategy.

文章介绍了一种新的控制框架来实现可控文本生成，并利用概率密度估计在隐藏空间中进行控制，该方法使用可逆变换函数，即 Normalizing Flow，将潜在空间中的复杂分布映射到先验空间中的简单高斯分布，实现了前向控制和反馈控制效果，达到了目前最好的效果。

通过概率密度估计在潜空间中实现可控文本生成

Controllable Text Generation via Probability Density Estimation in the Latent Space

Recent studies on StyleGAN show high performance on artistic portrait
generation by transfer learning with limited data. In this paper, we explore
more challenging exemplar-based high-resolution portrait style transfer by
introducing a novel DualStyleGAN with flexible control of dual styles of the
original face domain and the extended artistic portrait domain. Different from
StyleGAN, DualStyleGAN provides a natural way of style transfer by
characterizing the content and style of a portrait with an intrinsic style path
and a new extrinsic style path, respectively. The delicately designed extrinsic
style path enables our model to modulate both the color and complex structural
styles hierarchically to precisely pastiche the style example. Furthermore, a
novel progressive fine-tuning scheme is introduced to smoothly transform the
generative space of the model to the target domain, even with the above
modifications on the network architecture. Experiments demonstrate the
superiority of DualStyleGAN over state-of-the-art methods in high-quality
portrait style transfer and flexible style control.

本文介绍一种名为 DualStyleGAN 的新型神经网络，可用于基于范例的高分辨率肖像风格迁移，实现原始面部领域和艺术肖像领域的双重风格控制，并通过一种新型的渐进微调机制使其能够精确拼贴风格示例。实验证明其在高质量肖像风格转换和灵活的样式控制方面优于现有方法。

拼贴大师：基于样例的高分辨率肖像画风转换

Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

In this paper, we consider the problem to automatically reconstruct garment
and body shapes from a single near-front view RGB image. To this end, we
propose a layered garment representation on top of SMPL and novelly make the
skinning weight of garment independent of the body mesh, which significantly
improves the expression ability of our garment model. Compared with existing
methods, our method can support more garment categories and recover more
accurate geometry. To train our model, we construct two large scale datasets
with ground truth body and garment geometries as well as paired color images.
Compared with single mesh or non-parametric representation, our method can
achieve more flexible control with separate meshes, makes applications like
re-pose, garment transfer, and garment texture mapping possible. Code and some
data is available at this https URL

本文提出了一种基于 SMPL 的分层服装表示方法，通过使服装区分于身体网格并独立于其蒙皮权重，实现了更高的表达能力和更好的几何恢复效果，并结合训练数据集展示了其在服装重构和身体形状恢复方面的优势，同时也可以实现像重新姿势、服装转移和服装纹理映射等应用。