用3D高斯点实现可编辑2D图像，支持自然修改与物理模拟。
MiraGe: Editable 2D Images using Gaussian Splatting
- 通过镜像反射将2D图像映射到3D空间，用平面控制高斯点建模。
- 相比传统方法，渲染质量更高，支持人眼感知的自然编辑效果。
- 可与物理引擎结合，实现基于物理规律的图像动态修改。
隐式神经表示（INRs）通过连续函数逼近离散数据，常用于编码2D图像。传统基于图像的INRs使用神经网络将像素坐标映射为RGB值，通过权重捕捉形状、颜色和纹理。近期提出的GaussianImage采用高斯函数替代神经网络，在质量与压缩比上达到与经典INR模型相当的效果，但不支持图像修改。本文提出MiraGe，利用镜像反射将2D图像感知为3D空间中的结构，并采用平面控制高斯点实现精确2D图像编辑。该方法提升了渲染质量，支持类人感知的自然图像修改，通过3D空间建模实现了2D图像的3D式操作幻觉。同时，其高斯表示可轻松与物理引擎结合，实现基于物理规律的图像动态变化。MiraGe在质量上优于标准方法，并支持真实自然的2D图像修改。
原文摘要 · Abstract (English)
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
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