Many geometric quantities can be computed efficiently for convex meshes. For general meshes, methods for approximate convex decomposition have been developed that decompose a static, non-convex object into a small set of approximately convex parts. The convex hulls of those parts can then be used as a piecewise convex approximation to the original mesh. While previous work was only concerned with static meshes, we present a method for decomposing animated 3D meshes into temporally coherent approximately convex parts. Given a mesh and several training frames---that is, different spatial configurations of its vertices---we precompute an approximate convex decomposition that is independent of any specific frame. Such a decomposition can be transferred in real-time to novel, unseen frames. We apply our method to a variety of pre-animated meshes as well as a 3D character interactively controlled by a user's body pose. We further demonstrate that our method enables real-time physics simulations to interact with animated meshes.

Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps. Methods, performing only local operations in the vicinity of individual points, are very fast, but reconstructed models typically contain lots of holes. On the other hand, regularized volumetric approaches, formulated as a global optimization, are typically too slow for real-time interactive applications. We propose to use a regression forest based method, which predicts the projection of a grid point to the surface, depending on the spatial configuration of point density in the grid point neighborhood. We designed a suitable feature vector and efficient oct-tree based GPU evaluation, capable of predicting surface of high resolution 3D models in milliseconds. Our method learns and predicts surfaces from an observed point cloud sparser than the evaluation grid, and therefore effectively acts as a regularizer.

Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.

Abstract This paper introduces a combustion model of heat transfer and fuel consumption for the propagation of a fire front on a point cloud surface. The heat transfer includes the heat advection by the airflow as well as diffusion, chemical reaction, and heat loss to generate complex, but controllable heat flows with a designed airflow velocity. For the stable heat advection, we solve a semi‐Lagrangian method on point samples using discrete exponential maps to trace the position from which the wind blows while preserving the geodesic distance. We also propose angular Voronoi weights for a discrete Laplace‐Beltrami operator that shows better isotropic diffusion on the inhomogeneous distribution of point clouds than the cotangent or moving least‐squares schemes. We demonstrate a diversity of burning scenarios by incorporating factors affecting the fire spreading such as buoyancy and object geometries in the airflow velocity fields, or by synthesizing patterns.

Abstract We introduce a biologically motivated simulation technique for the realistic shape deformation of drying leaves. In contrast to skeleton‐based leaf deformation, our approach simulates the whole leaf surface to capture the fine details of desiccated leaves. We represent a leaf as a triangulated double‐layer structure that consists of a Delaunay triangulation discretized along the vein structure and its corresponding Voronoi diagram. This structure can generate not only sharp creases along leaf veins, but also the complicated curling and crumpling on the leaf surface. The loss of water is the major factor that controls the inhomogeneous shrinkage of drying leaves. The proposed osmotic water flow successfully models the gradual changes of dehydrated regions advancing towards the veins. We demonstrate the robustness of our method by comparing a sequence of simulated morphology changes with photographs of real leaves.