StylizedGS: Controllable Stylization for 3D Gaussian Splatting(related work)

Image Style Transfer

Style transfer aims to generate synthetic images with the artistic style of given images while preserving content.

neural style transfer methods involves iteratively optimizing the output image using Gram matrix loss and content loss calculated from VGG-Net extracted features.

Subsequent works have explored alternative style loss formulations to enhance semantic consistency and capture high-frequency style details such as brushstrokes.

Feed-forward transfer methods, where neural networks are trained to capture style information from the style image and transfer it to the input image in a single forward pass, ensuring faster stylization.

Recent improvements in style loss involve replacing the globa