Papers › Class-Continuous Conditional Generative Neural Radiance Field

Class-Continuous Conditional Generative Neural Radiance Field

3 Jan 2023arXiv:2301.00950archive 2025-07-28

Jiwook Kim, Minhyeok Lee

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.

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Tasks

3D-Aware Image SynthesisImage GenerationImage-to-Image TranslationNeRF

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation CelebA 128x128 C^3G-NeRF FID 7.64 #2 of 5 Archive leaderboard report
Image Generation CelebA 64x64 C^3G-NeRF FID 5.6 #22 of 39 Archive leaderboard report
Image-to-Image Translation AFHQ LPIPS 0.122 #2 of 2 Archive leaderboard report

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