桥梁行动:在照片之间生成3D姿势和形状
概括
这项研究介绍了ADAM-Net,这是一种创新的技术,用于在稀疏的照片之间生成现实的3D人体运动. 它使动画师和初学者能够轻松创建和编辑3D动画.
科学领域:
- 计算机图形 计算机图形
- 人工智能的人工智能
- 动画动画 动画动画
背景情况:
- 产生现实的3D人体运动对于游戏和动画行业至关重要.
- 现有的方法在从稀疏的视觉数据中创建行动之间的平稳过渡方面面临挑战.
研究的目的:
- 开发一种新的过渡生成技术,用于在稀疏的照片之间创建3D人体运动.
- 为了使3D动画师和初学者用户能够轻松地创建和编辑3D动作.
主要方法:
- 提出了一个自适应运动网络 (ADAM-Net) 来从蒙面动作序列中学习人类运动.
- 引入了一种随机掩盖过程,用于模拟从时间分散的照片中的过渡.
- 开发了长距离自适应运动 (L-ADAM) 和短距离自适应运动 (S-ADAM) 注意模块,以增强运动序列学习.
主要成果:
- 在给定的照片之间,ADAM-Net有效地生成了符合动态的3D姿势和形状.
- 该网络擅长生成3D运动之间的3D动作以及经典的3D人体姿势和形状估计任务.
- 在桥梁行动和创造现实的过渡方面表现出卓越的表现.
结论:
- ADAM-Net提供了一个强大的解决方案,用于从稀疏的视觉输入中生成现实的3D人体运动.
- 拟议的方法简化了用于各种应用的3D运动创建和编辑过程.
- 这项工作推进了3D人类运动合成和估计的最新技术.
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