  The human whole-body X-rays could offer a valuable reference for various
applications, including medical diagnostics, digital animation modeling, and
ergonomic design. The traditional method of obtaining X-ray information
requires the use of CT (Computed Tomography) scan machines, which emit
potentially harmful radiation. Thus it faces a significant limitation for
realistic applications because it lacks adaptability and safety. In our work,
We proposed a new method to directly generate the 2D human whole-body X-rays
from the human masking images. The predicted images will be similar to the real
ones with the same image style and anatomic structure. We employed a
data-driven strategy. By leveraging advanced generative techniques, our model
MaSkel(Masking image to Skeleton X-rays) could generate a high-quality X-ray
image from a human masking image without the need for invasive and harmful
radiation exposure, which not only provides a new path to generate highly
anatomic and customized data but also reduces health risks. To our knowledge,
our model MaSkel is the first work for predicting whole-body X-rays. In this
paper, we did two parts of the work. The first one is to solve the data
limitation problem, the diffusion-based techniques are utilized to make a data
augmentation, which provides two synthetic datasets for preliminary
pretraining. Then we designed a two-stage training strategy to train MaSkel. At
last, we make qualitative and quantitative evaluations of the generated X-rays.
In addition, we invite some professional doctors to assess our predicted data.
These evaluations demonstrate the MaSkel's superior ability to generate
anatomic X-rays from human masking images. The related code and links of the
dataset are available at https://github.com/2022yingjie/MaSkel.
