DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery
Journal:
arXiv
Published Date:
Apr 3, 2025
Abstract
In-bed human mesh recovery can be crucial and enabling for several healthcare
applications, including sleep pattern monitoring, rehabilitation support, and
pressure ulcer prevention. However, it is difficult to collect large real-world
visual datasets in this domain, in part due to privacy and expense constraints,
which in turn presents significant challenges for training and deploying deep
learning models. Existing in-bed human mesh estimation methods often rely
heavily on real-world data, limiting their ability to generalize across
different in-bed scenarios, such as varying coverings and environmental
settings. To address this, we propose a Sim-to-Real Transfer Framework for
in-bed human mesh recovery from overhead depth images, which leverages
large-scale synthetic data alongside limited or no real-world samples. We
introduce a diffusion model that bridges the gap between synthetic data and
real data to support generalization in real-world in-bed pose and body
inference scenarios. Extensive experiments and ablation studies validate the
effectiveness of our framework, demonstrating significant improvements in
robustness and adaptability across diverse healthcare scenarios.
