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风格化GS:可控制的风格化为3D高斯斯普拉特
IEEE transactions on pattern analysis and machine intelligence
|August 28, 2025
概括
使用3D高斯分辨率 (3DGS) 提供高效的3D神经风格传输,提高3D模型的几何精度和艺术控制. 这种方法增强了用户体验和创意灵活性.
科学领域:
- 计算机视觉
- 计算机图形
- 人工智能
背景情况:
- 3D生成和编辑对于XR技术的发展至关重要.
- 基于神经辐射场 (NeRF) 的3D造型方法面临效率和几何精度的限制.
- 灵活的艺术控制对于3D场景的造型是非常理想的.
研究的目的:
- 这是一个高效的3D神经风格转移框架.
- 在3D风格化中实现对感知因素的适应性控制.
- 解决现有方法中的效率和几何准确性问题.
主要方法:
- 使用3D高斯斯普拉特表示 (3DGS) 进行高效的3D风格化.
- 实现了基于过器的改进以删除文物和基于最近邻居的风格损失以微调几何和颜色.
- 嵌入深度保护损失和规则化以保持几何完整性.
主要成果:
- 实现高质量的3D造型与忠实笔画和几何一致性.
- 通过对颜色,尺度和区域的灵活用户控制来证明高效的风格化.
- 在风格化质量和推断速度方面表现优于现有方法.
结论:
- 提供一个高效可控的3D神经风格传输解决方案.
- 该框架增强了用户体验和3D内容创作的可能性.
- 在保存几何细节的同时将艺术风格转移到3D场景的强大方法.
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