Painting 3D Nature in 2D: View Synthesis of Natural Scenes From a Single Semantic Mask
Shangzhan Zhang · Sida Peng · Tianrun Chen · Linzhan Mou · Haotong Lin · Kaicheng Yu · Yiyi Liao · Xiaowei Zhou
West Building Exhibit Halls ABC 028
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3D-aware image synthesis either require multi-view supervision or learning category-level prior for specific classes of objects, which are inapplicable to natural scenes. Our key idea to solve this challenge is to use a semantic field as the intermediate representation, which is easier to reconstruct from an input semantic mask and then translated to a radiance field with the assistance of off-the-shelf semantic image synthesis models. Experiments show that our method outperforms baseline methods and produces photorealistic and multi-view consistent videos of a variety of natural scenes. The project website is https://zju3dv.github.io/paintingnature/.