StylizedGS：面向3D高斯泼溅的可控风格化
StylizedGS: Controllable Stylization for 3D Gaussian Splatting
AI总结：
针对现有NeRF类3D风格化方法效率低、几何风格迁移不准、控制灵活性不足的问题，提出基于3D高斯泼溅的StylizedGS框架，通过滤波去噪、最近邻风格损失与深度保持损失实现高质量风格化，支持颜色、尺度、区域的灵活控制，在效果与速度上均表现优异。
摘要：
随着XR（扩展现实）技术持续快速发展，3D生成与编辑的重要性日益凸显。其中，风格化在提升3D模型外观效果方面发挥着关键作用。借助风格化技术，用户仅需一张参考风格图像，就能在3D编辑中实现一致的艺术效果，是一种对用户友好的编辑方式。然而，当前基于NeRF（神经辐射场）的3D风格化方法存在效率问题，影响用户体验，且其隐式表征的特性限制了几何图案风格的精准迁移能力。此外，为营造有利于创意探索的环境，业界普遍认为艺术家需要具备对风格化场景进行灵活控制的能力。\n为解决上述问题，我们提出StylizedGS——一种基于3D Gaussian Splatting（3D高斯泼溅）表征的高效3D神经风格迁移框架，可对感知因素进行自适应控制。我们提出基于滤波器的优化方法，用于消除场景重建过程中影响风格化效果的漂浮物。引入基于最近邻的风格损失，通过微调3DGS的几何与颜色参数实现风格化，同时提出带其他正则化项的深度保持损失，防止几何内容被篡改。此外，借助专门设计的损失函数，StylizedGS支持用户在风格化过程中控制颜色、风格化尺度和作用区域，具备定制化能力。\n我们的方法实现了高质量的风格化效果，兼具忠实的笔触表现和几何一致性，且控制灵活。在多种场景和风格下开展的大量实验表明，该方法在风格化质量和推理速度方面均具备有效性与高效性。
英文摘要：
As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial. Among these, stylization plays a key role in enhancing the appearance of 3D models. By utilizing stylization, users can achieve consistent artistic effects in 3D editing using a single reference style image, making it a user-friendly editing method. However, recent NeRF-based 3D stylization methods encounter efficiency issues that impact the user experience, and their implicit nature limits their ability to accurately transfer geometric pattern styles. Additionally, the ability for artists to apply flexible control over stylized scenes is considered highly desirable to foster an environment conducive to creative exploration. To address the above issues, we introduce StylizedGS, an efficient 3D neural style transfer framework with adaptable control over perceptual factors based on 3D Gaussian Splatting representation. We propose a filter-based refinement to eliminate floaters that affect the stylization effects in the scene reconstruction process. The nearest neighbor-based style loss is introduced to achieve stylization by fine-tuning the geometry and color parameters of 3DGS, while a depth preservation loss with other regularizations is proposed to prevent the tampering of geometry content. Moreover, facilitated by specially designed losses, StylizedGS enables users to control color, stylized scale, and regions during the stylization to possess customization capabilities. Our method achieves high-quality stylization results characterized by faithful brushstrokes and geometric consistency with flexible controls. Extensive experiments across various scenes and styles demonstrate the effectiveness and efficiency of our method concerning both stylization quality and inference speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
