猜测:逐渐丰富的合成用于文字驱动的人类运动生成
IEEE transactions on visualization and computer graphics
|January 15, 2024
概括
本研究介绍了渐进式丰富合成 (GUESS),这是一种用于从文本中生成人类运动的新框架. 通过多层次的抽象,GUESS通过逐步完善运动细节来提高运动合成的准确性和现实性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 文本驱动的人类运动合成对于动画和虚拟现实等应用至关重要.
- 现有的方法往往难以从文本描述中产生准确和多样化的动作.
研究的目的:
- 为基于文本驱动的人类运动合成提出一种基于级联扩散的新型生成框架.
- 引入逐渐丰富的SyntheSis (GUESS) 战略,以改善运动生成.
主要方法:
- 在多个颗粒度层面上,GUESS循序渐进地将人类姿势抽象化为更粗的骨.
- 一个多阶段的框架与级联的潜伏扩散模型产生运动,从粗到详细.
- 一个动态的多条件融合机制平衡了文字和动作提示.
主要成果:
- 通过创建简洁和稳定的运动表示,GUESS框架显著有利于跨模态运动合成.
- 实验表明,在准确性,现实性和多样性方面,GUESS的表现优于最先进的方法.
- 级联扩散模型有效地从文本描述中生成详细的人类运动.
结论:
- 猜测提供了一个优越的方法,以文字驱动的人类运动合成.
- 多层抽象和级联生成框架提高了运动质量和控制.
- 这种方法推进了人工智能驱动的角色动画和运动生成领域.
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