Instant Photorealistic Neural Radiance Fields Stylization
This addresses the challenge of efficient and consistent 3D scene stylization for applications in graphics and visualization, representing an incremental improvement over existing neural radiance field methods.
The paper tackles the problem of stylizing 3D scenes from multi-view images by proposing a method that generates stylized novel views with consistent appearance across angles in under 10 minutes on modern GPUs, achieving superior results as validated by experiments.
We present Instant Neural Radiance Fields Stylization, a novel approach for multi-view image stylization for the 3D scene. Our approach models a neural radiance field based on neural graphics primitives, which use a hash table-based position encoder for position embedding. We split the position encoder into two parts, the content and style sub-branches, and train the network for normal novel view image synthesis with the content and style targets. In the inference stage, we execute AdaIN to the output features of the position encoder, with content and style voxel grid features as reference. With the adjusted features, the stylization of novel view images could be obtained. Our method extends the style target from style images to image sets of scenes and does not require additional network training for stylization. Given a set of images of 3D scenes and a style target(a style image or another set of 3D scenes), our method can generate stylized novel views with a consistent appearance at various view angles in less than 10 minutes on modern GPU hardware. Extensive experimental results demonstrate the validity and superiority of our method.