MiraGe: 使用高斯溅射的可编辑2D图像
MiraGe: Editable 2D Images using Gaussian Splatting
AI总结：
提出MiraGe方法，利用镜面反射在3D空间中感知2D图像，并通过平面控制的高斯函数实现精确编辑，提升渲染质量并支持物理仿真修改。
摘要：
隐式神经表示（INR）通过连续函数近似离散数据，常用于编码2D图像。传统的基于图像的INR使用神经网络将像素坐标映射到RGB值，在网络权重中捕获形状、颜色和纹理。最近，GaussianImage被提出作为一种替代方案，使用高斯函数代替神经网络以实现相当的质量和压缩。这种方案获得了与经典INR模型相似的质量和压缩比，但不允许图像修改。相比之下，我们的工作引入了一种新颖的方法MiraGe，它利用镜面反射在3D空间中感知2D图像，并使用平面控制的高斯函数进行精确的2D图像编辑。我们的方法提高了渲染质量，并允许逼真的图像修改，包括人类对3D世界中照片的感知。由于在3D空间中对图像进行建模，我们获得了2D图像中基于3D修改的幻觉。我们还展示了我们的高斯表示可以轻松与物理引擎结合，以产生基于物理的2D图像修改。因此，MiraGe允许比标准方法更好的质量和2D图像的自然修改。
英文摘要：
Implicit Neural Representations (INRs) approximate discrete data through continuous functions and are commonly used for encoding 2D images. Traditional image-based INRs employ neural networks to map pixel coordinates to RGB values, capturing shapes, colors, and textures within the network's weights. Recently, GaussianImage has been proposed as an alternative, using Gaussian functions instead of neural networks to achieve comparable quality and compression. Such a solution obtains a quality and compression ratio similar to classical INR models but does not allow image modification. In contrast, our work introduces a novel method, MiraGe, which uses mirror reflections to perceive 2D images in 3D space and employs flat-controlled Gaussians for precise 2D image editing. Our approach improves the rendering quality and allows realistic image modifications, including human-inspired perception of photos in the 3D world. Thanks to modeling images in 3D space, we obtain the illusion of 3D-based modification in 2D images. We also show that our Gaussian representation can be easily combined with a physics engine to produce physics-based modification of 2D images. Consequently, MiraGe allows for better quality than the standard approach and natural modification of 2D images
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。
