This paper introduces the Attribute-Decomposed GAN, a novel generative model
for controllable person image synthesis, which can produce realistic person
images with desired human attributes (e.g., pose, head, upper clothes and
pants) provided in various source inputs. The core idea of the proposed model
is to embed human attributes into the latent space as independent codes and
thus achieve flexible and continuous control of attributes via mixing and
interpolation operations in explicit style representations. Specifically, a new
architecture consisting of two encoding pathways with style block connections
is proposed to decompose the original hard mapping into multiple more
accessible subtasks. In source pathway, we further extract component layouts
with an off-the-shelf human parser and feed them into a shared global texture
encoder for decomposed latent codes. This strategy allows for the synthesis of
more realistic output images and automatic separation of un-annotated
attributes. Experimental results demonstrate the proposed method's superiority
over the state of the art in pose transfer and its effectiveness in the
brand-new task of component attribute transfer.

介绍了一种属性分解生成对抗网络，能够通过将人的属性嵌入潜在空间作为独立编码，通过混合和插值运算在显式样式空间中灵活控制属性，从而实现可控的人物图像生成，实现了更好的姿势和组件属性转换。

基于属性分解 GAN 的可控人物图像合成

Controllable Person Image Synthesis with Attribute-Decomposed GAN

We introduce an Expanded Parts Model (EPM) for recognizing human attributes
(e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in
still images. An EPM is a collection of part templates which are learnt
discriminatively to explain specific scale-space regions in the images (in
human centric coordinates). This is in contrast to current models which consist
of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a
subset of the parts to score an image and scores the image sparsely in space,
i.e. it ignores redundant and random background in an image. To learn our
model, we propose an algorithm which automatically mines parts and learns
corresponding discriminative templates together with their respective locations
from a large number of candidate parts. We validate our method on three recent
challenging datasets of human attributes and actions. We obtain convincing
qualitative and state-of-the-art quantitative results on the three datasets.

本文介绍了一种扩展部件模型（EPM），用于在静态图像中识别人物属性（例如年轻、短发、穿着西装）和行为（例如奔跑、跳跃）。EPM 仅使用部分部件评分，忽略冗余和随机背景，并通过算法从大量候选部件中自动挖掘部件并学习相应的鉴别模板及其位置。在三个挑战性的人体属性和动作数据集上验证了该方法，并得到了令人信服的定性和定量结果。

静态图像中描述人类语义的拓展部件模型

Expanded Parts Model for Semantic Description of Humans in Still Images

We propose a method for inferring human attributes (such as gender, hair
style, clothes style, expression, action) from images of people under large
variation of viewpoint, pose, appearance, articulation and occlusion.
Convolutional Neural Nets (CNN) have been shown to perform very well on large
scale object recognition problems. In the context of attribute classification,
however, the signal is often subtle and it may cover only a small part of the
image, while the image is dominated by the effects of pose and viewpoint.
Discounting for pose variation would require training on very large labeled
datasets which are not presently available. Part-based models, such as poselets
and DPM have been shown to perform well for this problem but they are limited
by shallow low-level features. We propose a new method which combines
part-based models and deep learning by training pose-normalized CNNs. We show
substantial improvement vs. state-of-the-art methods on challenging attribute
classification tasks in unconstrained settings. Experiments confirm that our
method outperforms both the best part-based methods on this problem and
conventional CNNs trained on the full bounding box of the person.

本文提出了一种结合基于部分模型和深度学习的姿势归一化卷积神经网络方法，以有效地从人物图片中推断人的属性，包括性别、发型、衣着风格、表情和动作，并且在限制条件下，该方法比目前最佳的基于部分模型和全身边框的 CNN 训练方法表现更好。