学习一个可解释的风格化子空间,用于3D意识的可动画艺术形式
IEEE transactions on visualization and computer graphics
|February 9, 2024
概括
3DArtmator通过启用3D意识的可动画重建和编辑来振兴静态绘画. 这种新的方法为交互式艺术探索创造了一个可解释的,风格化的潜在空间.
科学领域:
- 计算机视觉 计算机视觉
- 计算机图形 计算机图形
- 人工智能的人工智能
背景情况:
- 传统的绘画是静态的,限制了观众的互动.
- 现有的方法缺乏对艺术内容的解释性和精细的3D控制.
研究的目的:
- 介绍3DArtmator,这是一个用于3D意识的可动画重建和编辑艺术形式的新框架.
- 通过创建可解释的,风格化的潜伏空间,使静态杰作能够进行交互式探索.
主要方法:
- 建议采用两阶段的优化框架.
- 第一阶段在原始隐性空间中确定一个,以模仿艺术内容和姿势.
- 第二阶段训练了一个定制的3D感知生成对抗网络 (GAN),具有风格-定向差异损失,以保持潜伏结构.
主要成果:
- 3DArtmator成功地生成了3D重建,并使各种艺术风格的编辑成为可能.
- 该方法在风格化的潜空间中保持了可解释性和3D可控制性.
- 实验验证证证实了拟议方法的有效性和多功能性.
结论:
- 3DArtmator提供了一种通过交互式3D操纵来振兴艺术形式的新范式.
- 该框架为增强艺术参与和探索打开了道路.
- 可解读性和3D控制是数字艺术应用的关键贡献.
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