Fast and Accurate Parameter Conversion for Parametric Human Body Models 54

Parametric human body models, especially from the family of SMPL, are widely used in fields such as motion capture, 3D human pose and shape estimation, as well as for animation or synthesis of human motion. A problem that arises when different body model types are to be used in the same workflow is that model parameters are not interchangeable. Existing methods to convert parameters are based on optimization. While these methods achieve low conversion errors, they exhibit long runtimes, making them suboptimal for the conversion of real-time data or large datasets. In this paper, we improve on these aspects by presenting a deep learning-based conversion approach that is both accurate and fast, enabling the interplay of different body model types in real-time scenarios. To this end, we use a fully-connected neural network to jointly convert translation, shape, and pose parameters for different SMPL-based body models and show that we can accurately convert sequences from different datasets. Our method is able to provide feedback on its conversion quality, which can be used to enhance the trustworthiness of a superordinate processing pipeline. We also implement a demo application for the real-time streaming and visualization of digital human twins. We use our conversion method to transform parameters of different body model types to a common representation before streaming and show that we can do so in real-time even for sequences that are running at 120 Hz.