Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance
  Fields   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 pretrained
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.

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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: eess
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph