Using image morphing for memory-efficient impostor rendering on GPU

Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs; but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates

3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary materia

3D Point Capsule Networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data. 3D capsule networks arise as a direct consequence of our novel unified 3D auto-encoder formulation. Their dynamic routing scheme and the peculiar 2D latent space deployed by our approach bring in improvements for several common point cloud-related tasks, such as object classification, object reconstruction and part segmentation as substantiated by our extensive evaluations. Moreover, it enables new applications such as part interpolation and replacement