We present a CPU-based real-time cloth animation method for dressing virtual humans of various shapes and poses. Our approach formulates the clothing deformation as a high-dimensional function of body shape parameters and pose parameters. In order to accelerate the computation, our formulation factorizes the clothing deformation into two independent components: the deformation introduced by body pose variation (Clothing Pose Model) and the deformation from body shape variation (Clothing Shape Model). Furthermore, we sample and cluster the poses spanning the entire pose space and use those clusters to efficiently calculate the anchoring points. We also introduce a sensitivity-based distance measurement to both find nearby anchoring points and evaluate their contributions to the final animation. Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points. Compared to previous methods, our approach is general and able to add the shape dimension to any clothing pose model. Furthermore, we can animate clothing represented with tens of thousands of vertices at 50+ FPS on a CPU. We also conduct a user evaluation and show that our method can improve a user's perception of dressed virtual agents in an immersive virtual environment (IVE) compared to a realtime linear blend skinning method.

The cover image is based on the Original Article FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM Simulation by Wei Xiang et al., https://doi.org/10.1002/cav.1957.

Abstract Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high‐fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial‐and‐error processes. To address both challenges, we present a novel data‐driven framework, FASTSWARM, to model complex behaviors of flying insects based on real‐world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real‐time performance for large swarms. The high‐fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, self‐propelled behaviors such as target following and obstacle avoidance, and other characteristics such as random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modeled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real‐world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable.