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.
隐式神经表示（INRs）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INRs使用神经网络将像素坐标映射到RGB值，捕捉形状、颜色和纹理，这些信息存储在网络的权重中。最近，提出了GaussianImage作为替代方案，使用高斯函数代替神经网络来实现相似的质量和压缩效果。虽然这种方法在质量和压缩比上与经典INR模型相当，但不允许图像修改。相反，我们的工作提出了一种新方法，MiraGe，它通过镜面反射在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。我们的方法不仅提高了渲染质量，还允许进行逼真的图像修改，包括模拟人在3D世界中对照片的感知。通过在3D空间中对图像建模，我们实现了在2D图像中进行3D效果修改的错觉。我们还展示了我们的高斯表示可以轻松与物理引擎结合，实现基于物理的2D图像修改。因此，MiraGe相比标准方法提供了更高的质量，并能够自然地修改2D图像。