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We introduce a novel approach that takes a single semantic mask as input tosynthesize multi-view consistent color images of natural scenes, trained with acollection of single images from the Internet. Prior works on 3D-aware imagesynthesis either require multi-view supervision or learning category-levelprior for specific classes of objects, which can hardly work for naturalscenes. Our key idea to solve this challenging problem is to use a semanticfield as the intermediate representation, which is easier to reconstruct froman input semantic mask and then translate to a radiance field with theassistance of off-the-shelf semantic image synthesis models. Experiments showthat our method outperforms baseline methods and produces photorealistic,multi-view consistent videos of a variety of natural scenes.

Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask