Human pose transfer, as a misaligned image generation task, is very
challenging. Existing methods cannot effectively utilize the input information,
which often fail to preserve the style and shape of hair and clothes. In this
paper, we propose an adaptive human pose transfer network with two hierarchical
deformation levels. The first level generates human semantic parsing aligned
with the target pose, and the second level generates the final textured person
image in the target pose with the semantic guidance. To avoid the drawback of
vanilla convolution that treats all the pixels as valid information, we use
gated convolution in both two levels to dynamically select the important
features and adaptively deform the image layer by layer. Our model has very few
parameters and is fast to converge. Experimental results demonstrate that our
model achieves better performance with more consistent hair, face and clothes
with fewer parameters than state-of-the-art methods. Furthermore, our method
can be applied to clothing texture transfer.

本文提出了一个基于两级分层变形的自适应人体姿态转移网络，使用门控卷积动态选择图像的重要特征并逐层自适应变形，通过更少的参数快速收敛并在服装纹理转移等任务中获得更好的性能。

通过自适应分层变形实现人体姿势转换

Human Pose Transfer by Adaptive Hierarchical Deformation

Virtual try-on system under arbitrary human poses has huge application
potential, yet raises quite a lot of challenges, e.g. self-occlusions, heavy
misalignment among diverse poses, and diverse clothes textures. Existing
methods aim at fitting new clothes into a person can only transfer clothes on
the fixed human pose, but still show unsatisfactory performances which often
fail to preserve the identity, lose the texture details, and decrease the
diversity of poses. In this paper, we make the first attempt towards multi-pose
guided virtual try-on system, which enables transfer clothes on a person image
under diverse poses. Given an input person image, a desired clothes image, and
a desired pose, the proposed Multi-pose Guided Virtual Try-on Network (MG-VTON)
can generate a new person image after fitting the desired clothes into the
input image and manipulating human poses. Our MG-VTON is constructed in three
stages: 1) a desired human parsing map of the target image is synthesized to
match both the desired pose and the desired clothes shape; 2) a deep Warping
Generative Adversarial Network (Warp-GAN) warps the desired clothes appearance
into the synthesized human parsing map and alleviates the misalignment problem
between the input human pose and desired human pose; 3) a refinement render
utilizing multi-pose composition masks recovers the texture details of clothes
and removes some artifacts. Extensive experiments on well-known datasets and
our newly collected largest virtual try-on benchmark demonstrate that our
MG-VTON significantly outperforms all state-of-the-art methods both
qualitatively and quantitatively with promising multi-pose virtual try-on
performances.

本文提出了首个多姿态影响下的虚拟试衣系统，使用三阶段的网络包括合成期望的解剖结构映射，将解剖结构的映射和服装特征整合起来，以及使用多姿态合成蒙版进行修复，成功地解决了虚拟试衣中的多种问题，实验结果表明，该虚拟试衣系统和该多姿态影响下的虚拟试衣技术显著优于现有技术。