  Diffusion models have marked a significant milestone in the enhancement of
image and video generation technologies. However, generating videos that
precisely retain the shape and location of moving objects such as robots
remains a challenge. This paper presents diffusion models specifically tailored
to generate videos that accurately maintain the shape and location of mobile
robots. This development offers substantial benefits to those working on
detecting dangerous interactions between humans and robots by facilitating the
creation of training data for collision detection models, circumventing the
need for collecting data from the real world, which often involves legal and
ethical issues. Our models incorporate techniques such as embedding accessible
robot pose information and applying semantic mask regulation within the
ConvNext backbone network. These techniques are designed to refine intermediate
outputs, therefore improving the retention performance of shape and location.
Through extensive experimentation, our models have demonstrated notable
improvements in maintaining the shape and location of different robots, as well
as enhancing overall video generation quality, compared to the benchmark
diffusion model. Codes will be opensourced at
\href{https://github.com/PengPaulWang/diffusion-robots}{Github}.
