Image-based virtual try-on is one of the most promising applications of
human-centric image generation due to its tremendous real-world potential. Yet,
as most try-on approaches fit in-shop garments onto a target person, they
require the laborious and restrictive construction of a paired training
dataset, severely limiting their scalability. While a few recent works attempt
to transfer garments directly from one person to another, alleviating the need
to collect paired datasets, their performance is impacted by the lack of paired
(supervised) information. In particular, disentangling style and spatial
information of the garment becomes a challenge, which existing methods either
address by requiring auxiliary data or extensive online optimization
procedures, thereby still inhibiting their scalability. To achieve a
\emph{scalable} virtual try-on system that can transfer arbitrary garments
between a source and a target person in an unsupervised manner, we thus propose
a texture-preserving end-to-end network, the PAtch-routed SpaTially-Adaptive
GAN (PASTA-GAN), that facilitates real-world unpaired virtual try-on.
Specifically, to disentangle the style and spatial information of each garment,
PASTA-GAN consists of an innovative patch-routed disentanglement module for
successfully retaining garment texture and shape characteristics. Guided by the
source person keypoints, the patch-routed disentanglement module first
decouples garments into normalized patches, thus eliminating the inherent
spatial information of the garment, and then reconstructs the normalized
patches to the warped garment complying with the target person pose. Given the
warped garment, PASTA-GAN further introduces novel spatially-adaptive residual
blocks that guide the generator to synthesize more realistic garment details.

提出了一种无监督的图像虚拟试穿系统 PASTA-GAN，通过创新的贴片路由 module 成功保留服装的纹理和形状特征，引入新的空间自适应残差块来指导生成器合成更真实的服装细节。

通过 Patch-Routed 空间自适应 GAN 实现可扩展的非配对虚拟试穿

Towards Scalable Unpaired Virtual Try-On via Patch-Routed  Spatially-Adaptive GAN

Video frame interpolation typically involves two steps: motion estimation and
pixel synthesis. Such a two-step approach heavily depends on the quality of
motion estimation. This paper presents a robust video frame interpolation
method that combines these two steps into a single process. Specifically, our
method considers pixel synthesis for the interpolated frame as local
convolution over two input frames. The convolution kernel captures both the
local motion between the input frames and the coefficients for pixel synthesis.
Our method employs a deep fully convolutional neural network to estimate a
spatially-adaptive convolution kernel for each pixel. This deep neural network
can be directly trained end to end using widely available video data without
any difficult-to-obtain ground-truth data like optical flow. Our experiments
show that the formulation of video interpolation as a single convolution
process allows our method to gracefully handle challenges like occlusion, blur,
and abrupt brightness change and enables high-quality video frame
interpolation.

通过使用一个深度卷积神经网络，将视频帧的插值过程作为一个单一卷积过程来处理，可以更好地解决运动估计质量的问题，并且能够优雅地处理一些挑战如遮挡，模糊和亮度变化，从而实现高质量的视频帧插值。