Dynamic garment simulation plays a crucial role in applications such as virtual try-on and film production. Existing simulation methods face challenges including high computational time, video frame jitter, and limited garment styles. Therefore, we propose SmPhy, a method that takes real videos as input. To alleviate frame jitter in video generation, we employ a temporal perception network for motion smoothing. The temporal physics garment module introduces temporal dependency, utilizing the garment information output from the current frame as input for the next frame, and provides reliable physical constraints to enhance garment deformation effects. Qualitative and quantitative experiments demonstrate that SmPhy reduces time costs and successfully simulates 3D clothing animations closely resembling real-world behaviors. Access links to supporting materials are as follows: https://drive.google.com/file/d/1BIbSI4mT4YgCVFRorszGW9pxbPZo40SH/view?usp=drive_link

Virtual garment animation and deformation constitute a pivotal research direction in computer graphics, finding extensive applications in domains such as computer games, animation, and film. Traditional physics-based methods can simulate the physical characteristics of garments, such as elasticity and gravity, to generate realistic deformation effects. However, the computational complexity of such methods hinders real-time animation generation. Data-driven approaches, on the other hand, learn from existing garment deformation data, enabling rapid animation generation. Nevertheless, animations produced using this approach often lack realism, struggling to capture subtle variations in garment behavior. We proposes an approach that balances realism and speed, by considering both spatial and temporal dimensions, we leverage real-world videos to capture human motion and garment deformation, thereby producing more realistic animation effects. We address the complexity of spatiotemporal attention by aligning input features and calculating spatiotemporal attention at each spatial position in a batch-wise manner. For garment deformation, garment segmentation techniques are employed to extract garment templates from videos. Subsequently, leveraging our designed Transformer-based temporal framework, we capture the correlation between garment deformation and human body shape features, as well as frame-level dependencies. Furthermore, we utilize a feature fusion strategy to merge shape and motion features, addressing penetration issues between clothing and the human body through post-processing, thus generating collision-free garment deformation sequences. Qualitative and quantitative experiments demonstrate the superiority of our approach over existing methods, efficiently producing temporally coherent and realistic dynamic garment deformations. • Both spatial and temporal dimensions are considered in terms of human movement. • Feature parameter fusion strategy to integrate human shape and motion features. • The attentional mechanism establishes dependencies between garment frames. • We resolve the garment-body interpenetration issue through post-processing.

The goal of digital dress body animation is to produce the most realistic dress body animation possible. Although a method based on the same topology as the body can produce realistic results, it can only be applied to garments with the same topology as the body. Although the generalization-based approach can be extended to different types of garment templates, it still produces effects far from reality. We propose GSNet, a learning-based model that generates realistic garment animations and applies to garment types that do not match the body topology. We encode garment templates and body motions into latent space and use graph convolution to transfer body motion information to garment templates to drive garment motions. Our model considers temporal dependency and provides reliable physical constraints to make the generated animations more realistic. Qualitative and quantitative experiments show that our approach achieves state-of-the-art 3D garment animation performance.