mlsanityData to 7 Oct 2026

Class-Continuous Conditional Generative Neural Radiance Field

arXiv 2301.00950 ↗First posted 3 Jan 2023cs.CV cs.AIcited by 1

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Abstract (arXiv)

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF (C3G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed C3G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, C3G-NeRF exhibits a Fr'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a 1282 resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with C3G-NeRF.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 36 of its 53 references that are in the map. First posted on arXiv 3 Jan 2023.

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36 of 53 references.

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24 more
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  1. 2023Counterfactual Edits for Generative Evaluationcited by 0