RGB-D based framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out

Generative shape deformation with optimal transport using learned transformations

International audienceShape deformation is a fundamental problem in computer graphics and computer vision, with numerous appli- cations in fields such as animation, medical imaging, robotics to cite a few. We propose a method for shape deformation based on applying learned transformations with optimal transport (OT). Our method combines the power of the latter with the flexibility of learned transformations to provide an efficient and effective solution for 2D and 3D shape deformation. We formulate the problem as an OT task, where the goal is to learn the optimal way to move the mass distribution of a shape to another. We then use the learned geometric transformations, to achieve shape deformation. Our method can be applied to a wide range of shapes and applications. Interestingly, we show that it requires a small amount of data to learn the transformations. We demonstrate the performance of our method on our own crafted dataset of 2D and 3D shapes and evaluate its effectiveness using various metrics. The promising results obtained suggest that our method can be applied in a wide range of real-world applications