基于可控制粒子的实时XR可编辑网格动画建模。

Xiangyang Zhou;Yanrui Xu;Chao Yao;Xiaokun Wang;Xiaojuan Ban
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引用次数: 0

摘要

在XR应用中实时生成可编辑的网格动画一直是XR领域的研究热点。然而,轻松控制生成的可编辑网格仍然是一个重大挑战。现有方法生成速度慢,结果不理想,不能准确模拟目标物体的复杂细节和形状,不能满足用户的期望。此外,最终生成的网格通常需要用户手动调整,并且难以同时生成多个目标模型。为了克服这些限制,提出了一种基于目标采样特征的粒子通用控制方案。引入了一种空间自适应的粒子耦合控制算法,根据模型采样的空间特征调整控制力的大小,从而消除了参数依赖,实现了对同一场景内多种类型模型的控制。我们进一步引入边界校正技术来提高生成目标形状的精度,同时减少粒子飞溅。此外,一个距离自适应的颗粒破碎机制,防止不必要的颗粒积累。实验结果表明,与现有方法相比,该方法在控制复杂结构和同时生成多个目标方面具有更好的性能。在稀疏模型采样条件下,提高了对复杂结构和目标的控制精度。在保持高稳定性和效率的同时,它也始终如一地提供出色的结果。最终,我们能够创建一组平滑的可编辑网格,并开发了将该算法集成到VR和AR动画应用程序中的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Editable Mesh Animations Modeling Based on Controlable Particles for Real-Time XR
The real-time generation of editable mesh animations in XR applications has been a focal point of research in the XR field. However, easily controlling the generated editable meshes remains a significant challenge. Existing methods often suffer from slow generation speeds and suboptimal results, failing to accurately simulate target objects' complex details and shapes, which does not meet user expectations. Additionally, the final generated meshes typically require manual user adjustments, and it is difficult to generate multiple target models simultaneously. To overcome these limitations, a universal control scheme for particles based on the sampling features of the target is proposed. It introduces a spatially adaptive control algorithm for particle coupling by adjusting the magnitude of control forces based on the spatial features of model sampling, thereby eliminating the need for parameter dependency and enabling the control of multiple types of models within the same scene. We further introduce boundary correction techniques to improve the precision in generating target shapes while reducing particle splashing. Moreover, a distance-adaptive particle fragmentation mechanism prevents unnecessary particle accumulation. Experimental results demonstrate that the method has better performance in controlling complex structures and generating multiple targets at the same time compared to existing methods. It enhances control accuracy for complex structures and targets under the condition of sparse model sampling. It also consistently delivers outstanding results while maintaining high stability and efficiency. Ultimately, we were able to create a set of smooth editable meshes and developed a solution for integrating this algorithm into VR and AR animation applications.
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