Garment transfer shows great potential in realistic applications with the
goal of transfering outfits across different people images. However, garment
transfer between images with heavy misalignments or severe occlusions still
remains as a challenge. In this work, we propose Complementary Transfering
Network (CT-Net) to adaptively model different levels of geometric changes and
transfer outfits between different people. In specific, CT-Net consists of
three modules: 1) A complementary warping module first estimates two
complementary warpings to transfer the desired clothes in different
granularities. 2) A layout prediction module is proposed to predict the target
layout, which guides the preservation or generation of the body parts in the
synthesized images. 3) A dynamic fusion module adaptively combines the
advantages of the complementary warpings to render the garment transfer
results. Extensive experiments conducted on DeepFashion dataset demonstrate
that our network synthesizes high-quality garment transfer images and
significantly outperforms the state-of-art methods both qualitatively and
quantitatively.

该论文提出了 Complementary Transfering Network（CT-Net）模型，其中包括了互补配准模块、布局预测模块、动态融合模块等三部分，通过在 DeepFashion 数据集上的实验，证明了本研究的高质量服装迁移图像合成能力，并在品质和数量上显著优于现有方法。

CT-Net：基于补充转移网络的服装转移方法

CT-Net: Complementary Transfering Network for Garment Transfer with  Arbitrary Geometric Changes

Joint detection of drivable areas and road anomalies is very important for
mobile robots. Recently, many semantic segmentation approaches based on
convolutional neural networks (CNNs) have been proposed for pixel-wise drivable
area and road anomaly detection. In addition, some benchmark datasets, such as
KITTI and Cityscapes, have been widely used. However, the existing benchmarks
are mostly designed for self-driving cars. There lacks a benchmark for ground
mobile robots, such as robotic wheelchairs. Therefore, in this paper, we first
build a drivable area and road anomaly detection benchmark for ground mobile
robots, evaluating the existing state-of-the-art single-modal and data-fusion
semantic segmentation CNNs using six modalities of visual features.
Furthermore, we propose a novel module, referred to as the dynamic fusion
module (DFM), which can be easily deployed in existing data-fusion networks to
fuse different types of visual features effectively and efficiently. The
experimental results show that the transformed disparity image is the most
informative visual feature and the proposed DFM-RTFNet outperforms the
state-of-the-arts. Additionally, our DFM-RTFNet achieves competitive
performance on the KITTI road benchmark. Our benchmark is publicly available at
this https URL

本文提出了一个针对地面移动机器人的可行驶区域和路面异常检测基准，评估了现有单模和数据融合语义分割 CNN 的性能，并提出了一个名为动态融合模块（DFM）的新模块来有效和高效地融合不同类型的视觉特征。实验结果表明，变换视差图是最具信息量的视觉特征，提出的 DFM-RTFNet 在 KITTI 路况基准上表现出色，并在公开数据集上可用。