In computer vision, human pose synthesis and transfer deal with probabilistic
image generation of a person in a previously unseen pose from an already
available observation of that person. Though researchers have recently proposed
several methods to achieve this task, most of these techniques derive the
target pose directly from the desired target image on a specific dataset,
making the underlying process challenging to apply in real-world scenarios as
the generation of the target image is the actual aim. In this paper, we first
present the shortcomings of current pose transfer algorithms and then propose a
novel text-based pose transfer technique to address those issues. We divide the
problem into three independent stages: (a) text to pose representation, (b)
pose refinement, and (c) pose rendering. To the best of our knowledge, this is
one of the first attempts to develop a text-based pose transfer framework where
we also introduce a new dataset DF-PASS, by adding descriptive pose annotations
for the images of the DeepFashion dataset. The proposed method generates
promising results with significant qualitative and quantitative scores in our
experiments.

本研究提出了一种三步走的文本姿势转移方法，解决了现有姿势转移算法的缺陷，并在 DeepFashion 数据集上新增了姿势注释，通过实验得出了显著的定量和定性分数。