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arXiv 2021年

作者： Pan, Xiaoyu Huang, Jiancong Mai, Jiaming Wang, He Li, Honglin Su, Tongkui Wang, Wenjun Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University China ZJU-Tencent Game Intelligent Graphics Innovation Technology Joint Lab Hangzhou China Tencent Games Lightspeed & Quantum Studios Shenzhen China School of Computing University of Leeds Leeds United Kingdom Quanzhou Medical College Quanzhou China Tencent Institute of Games Shenzhen China

Character rigging is universally needed in computer graphics but notoriously laborious. We present a new method, HeterSkinNet, aiming to fully automate such processes and significantly boost productivity. Given a character mesh and skeleton as input, our method builds a heterogeneous graph that treats the mesh vertices and the skeletal bones as nodes of different types and uses graph convolutions to learn their relationships. To tackle the graph heterogeneity, we propose a new graph network convolution operator that transfers information between heterogeneous nodes. The convolution is based on a new distance HollowDist that quantifies the relations between mesh vertices and bones. We show that HeterSkinNet is robust for production characters by providing the ability to incorporate meshes and skeletons with arbitrary topologies and morphologies (e.g., out-of-body bones, disconnected mesh components, etc.). Through exhaustive comparisons, we show that HeterSkinNet outperforms state-of-the-art methods by large margins in terms of rigging accuracy and naturalness. HeterSkinNet provides a solution for effective and robust character rigging. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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fastswarm: a data-driven framework for real-time flying insect SWARM simulation

arXiv

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arXiv 2020年

作者： Xiang, Wei Yao, Xinran Wang, He Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University Hangzhou310058 China School of Computing University of Leeds LeedsLS2 9JT United Kingdom ZJU-Tencent Game and Intelligent Graphics Innovation Technology Joint Lab Hangzhou310058 China

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, the self-propelled behaviors such as target following and obstacle avoidance, and other characteristics such as the random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modelled 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. Copyright © 2020, The Authors. All rights reserved.

关键词： Animation

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