Synthesizing garment dynamics according to body motions is a vital technique in computer graphics.Physics-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to i...Synthesizing garment dynamics according to body motions is a vital technique in computer graphics.Physics-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to implement,and complex to control.Existing data-driven approaches either lack temporal consistency,or fail to handle garments that are different from body topology.In this paper,we present a motion-inspired real-time garment synthesis workflow that enables high-level control of garment shape.Given a sequence of body motions,our workflow is able to gen-erate corresponding garment dynamics with both spatial and temporal coherence.To that end,we develop a transformer-based garment synthesis network to learn the mapping from body motions to garment dynamics.Frame-level attention is employed to capture the dependency of garments and body motions.Moreover,a post-processing procedure is further tak-en to perform penetration removal and auto-texturing.Then,textured clothing animation that is collision-free and tempo-rally-consistent is generated.We quantitatively and qualitatively evaluated our proposed workflow from different aspects.Extensive experiments demonstrate that our network is able to deliver clothing dynamics which retain the wrinkles from the physics-based simulation,while running 1000 times faster.Besides,our workflow achieved superior synthesis perfor-mance compared with alternative approaches.To stimulate further research in this direction,our code will be publicly available soon.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61972379.
文摘Synthesizing garment dynamics according to body motions is a vital technique in computer graphics.Physics-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to implement,and complex to control.Existing data-driven approaches either lack temporal consistency,or fail to handle garments that are different from body topology.In this paper,we present a motion-inspired real-time garment synthesis workflow that enables high-level control of garment shape.Given a sequence of body motions,our workflow is able to gen-erate corresponding garment dynamics with both spatial and temporal coherence.To that end,we develop a transformer-based garment synthesis network to learn the mapping from body motions to garment dynamics.Frame-level attention is employed to capture the dependency of garments and body motions.Moreover,a post-processing procedure is further tak-en to perform penetration removal and auto-texturing.Then,textured clothing animation that is collision-free and tempo-rally-consistent is generated.We quantitatively and qualitatively evaluated our proposed workflow from different aspects.Extensive experiments demonstrate that our network is able to deliver clothing dynamics which retain the wrinkles from the physics-based simulation,while running 1000 times faster.Besides,our workflow achieved superior synthesis perfor-mance compared with alternative approaches.To stimulate further research in this direction,our code will be publicly available soon.
