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

• URL: http://arxiv.org/abs/2302.07224v1
• Date: Tue, 14 Feb 2023 17:57:58 GMT
• Title: Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
• Authors: Shangzhan Zhang, Sida Peng, Tianrun Chen, Linzhan Mou, Haotong Lin, Kaicheng Yu, Yiyi Liao, Xiaowei Zhou
• Abstract summary: We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes. Our method outperforms baseline methods and produces photorealistic, multi-view consistent videos of a variety of natural scenes.
• Score: 29.38152100352871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 can hardly work for natural scenes. Our key idea to solve this challenging problem is to use a semantic field as the intermediate representation, which is easier to reconstruct from an input semantic mask and then translate 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, multi-view consistent videos of a variety of natural scenes.

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