论文标题
SEM2NERF：将单视语义面具转换为神经辐射场
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
论文作者
论文摘要
图像翻译和操纵随着深层生成模型的快速发展而引起了越来越多的关注。尽管现有的方法带来了令人印象深刻的结果，但它们主要在2D空间中运行。鉴于基于NERF的3D感知生成模型的最新进展，我们介绍了一项新的任务，语义到nerf翻译，旨在重建由NERF建模的3D场景，该场景以一个单视语义掩码作为输入为条件。为了启动这项新颖的任务，我们提出了SEM2NERF框架。特别是，SEM2NERF通过将语义面膜编码到控制预训练的解码器的3D场景表示形式中来解决高度挑战的任务。为了进一步提高映射的准确性，我们将新的区域感知学习策略集成到编码器和解码器的设计中。我们验证了所提出的SEM2NERF的功效，并证明它在两个基准数据集上的表现优于几个强基础。代码和视频可在https://donydchen.github.io/sem2nerf/上找到
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive results, they mainly operated in 2D space. In light of recent advances in NeRF-based 3D-aware generative models, we introduce a new task, Semantic-to-NeRF translation, that aims to reconstruct a 3D scene modelled by NeRF, conditioned on one single-view semantic mask as input. To kick-off this novel task, we propose the Sem2NeRF framework. In particular, Sem2NeRF addresses the highly challenging task by encoding the semantic mask into the latent code that controls the 3D scene representation of a pre-trained decoder. To further improve the accuracy of the mapping, we integrate a new region-aware learning strategy into the design of both the encoder and the decoder. We verify the efficacy of the proposed Sem2NeRF and demonstrate that it outperforms several strong baselines on two benchmark datasets. Code and video are available at https://donydchen.github.io/sem2nerf/
