We present a framework for diversifying human poses in a synthetic dataset
for aerial-view human detection. Our method firstly constructs a set of novel
poses using a pose generator and then alters images in the existing synthetic
dataset to assume the novel poses while maintaining the original style using an
image translator. Since images corresponding to the novel poses are not
available in training, the image translator is trained to be applicable only
when the input and target poses are similar, thus training does not require the
novel poses and their corresponding images. Next, we select a sequence of
target novel poses from the novel pose set, using Dijkstra's algorithm to
ensure that poses closer to each other are located adjacently in the sequence.
Finally, we repeatedly apply the image translator to each target pose in
sequence to produce a group of novel pose images representing a variety of
different limited body movements from the source pose. Experiments demonstrate
that, regardless of how the synthetic data is used for training or the data
size, leveraging the pose-diversified synthetic dataset in training generally
presents remarkably better accuracy than using the original synthetic dataset
on three aerial-view human detection benchmarks (VisDrone, Okutama-Action, and
ICG) in the few-shot regime.

我们提出了一个用于多样化合成数据集中人体姿势的框架，该框架利用姿势生成器构造一组新颖的姿势，并使用图像翻译器改变现有合成数据集中的图像以呈现新颖的姿势，同时保持原始风格。通过实验证明，无论合成数据如何被用于训练，以及数据规模如何，利用多样化的合成数据集进行训练都比在少样本情况下使用原始合成数据集对于三个航拍人体检测基准（VisDrone，Okutama-Action 和 ICG）的准确性通常明显更好。

多样化的合成数据中的人体姿态对于航空视角人体检测的作用

Diversifying Human Pose in Synthetic Data for Aerial-view Human  Detection

Aerial-view human detection has a large demand for large-scale data to
capture more diverse human appearances compared to ground-view human detection.
Therefore, synthetic data can be a good resource to expand data, but the domain
gap with real-world data is the biggest obstacle to its use in training. As a
common solution to deal with the domain gap, the sim2real transformation is
used, and its quality is affected by three factors: i) the real data serving as
a reference when calculating the domain gap, ii) the synthetic data chosen to
avoid the transformation quality degradation, and iii) the synthetic data pool
from which the synthetic data is selected. In this paper, we investigate the
impact of these factors on maximizing the effectiveness of synthetic data in
training in terms of improving learning performance and acquiring domain
generalization ability--two main benefits expected of using synthetic data. As
an evaluation metric for the second benefit, we introduce a method for
measuring the distribution gap between two datasets, which is derived as the
normalized sum of the Mahalanobis distances of all test data. As a result, we
have discovered several important findings that have never been investigated or
have been used previously without accurate understanding. We expect that these
findings can break the current trend of either naively using or being hesitant
to use synthetic data in machine learning due to the lack of understanding,
leading to more appropriate use in future research.

本文研究了航拍人体检测中利用合成数据来训练的方法，通过探讨实际数据、合成数据和数据池选择对合成数据的质量影响以及利用合成数据提高学习性能和域泛化能力的效果，发现了几个重要的发现，为未来研究提供了更合理的使用合成数据的方向。