Sem2NeRF: converting single-view semantic masks to neural radiance fields

Yuedong Chen, Qianyi Wu, Chuanxia Zheng, Tat Jen Cham, Jianfei Cai

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


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

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2022 - 17th European Conference Tel Aviv, Israel, October 23–27, 2022 Proceedings, Part XIV
EditorsShai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner
Place of PublicationCham Switzerland
Number of pages19
ISBN (Electronic)9783031197819
ISBN (Print)9783031197802
Publication statusPublished - 2022
EventEuropean Conference on Computer Vision 2022 - Tel Aviv, Israel
Duration: 23 Oct 202227 Oct 2022
Conference number: 17th (Proceedings) (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Computer Vision 2022
Abbreviated titleECCV 2022
CityTel Aviv
Internet address


  • 3D deep learning
  • Conditional generative model
  • Image-to-image translation
  • NeRF-based generation
  • Neural radiance fields

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