mlsanityData to 7 Oct 2026

CG-NeRF: Conditional Generative Neural Radiance Fields

arXiv 2112.03517 ↗First posted 7 Dec 2021cs.CV cs.GRcited by 18

Not on the map. Why?

Abstract (arXiv)

While recent NeRF-based generative models achieve the generation of diverse 3D-aware images, these approaches have limitations when generating images that contain user-specified characteristics. In this paper, we propose a novel model, referred to as the conditional generative neural radiance fields (CG-NeRF), which can generate multi-view images reflecting extra input conditions such as images or texts. While preserving the common characteristics of a given input condition, the proposed model generates diverse images in fine detail. We propose: 1) a novel unified architecture which disentangles the shape and appearance from a condition given in various forms and 2) the pose-consistent diversity loss for generating multimodal outputs while maintaining consistency of the view. Experimental results show that the proposed method maintains consistent image quality on various condition types and achieves superior fidelity and diversity compared to existing NeRF-based generative models.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 26 of its 36 references that are in the map. First posted on arXiv 7 Dec 2021.

Builds on

26 of 36 references.

References · 26

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  4. 2016Improved Techniques for Training GANscited by 2,938
  5. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  6. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
  7. 2019Improved Precision and Recall Metric for Assessing Generative Modelscited by 925
  8. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 857
  9. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 784
  10. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
  11. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  12. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
14 more
  1. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
  2. 2018Which Training Methods for GANs do actually Converge?cited by 672
  3. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  4. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
  5. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
  6. 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 465
  7. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  8. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 425
  9. 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
  10. 2021In-Place Scene Labelling and Understanding with Implicit Scene Representationcited by 411
  11. 2020AutoInt: Automatic Integration for Fast Neural Volume Renderingcited by 204
  12. 2019Diversity-Sensitive Conditional Generative Adversarial Networkscited by 77
  13. 2021On Aliased Resizing and Surprising Subtleties in GAN Evaluationcited by 51
  14. 2021FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modellingcited by 47

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 6; 2023: 6; 2024: 3; 2025: 1.

18 papers.

Sort:

By area:

  1. 2022NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)cited by 156
  2. 2022VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Gridscited by 133
  3. 2022EpiGRAF: Rethinking training of 3D GANscited by 108
  4. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  5. 2022One-shot Implicit Animatable Avatars with Model-based Priorscited by 38
  6. 2022NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fieldscited by 33
  7. 2021Representing 3D Shapes with Probabilistic Directed Distance Fieldscited by 21
  8. 2023Neural Radiance Fields: Past, Present, and Futurecited by 8
  9. 2023NaviNeRF: NeRF-based 3D Representation Disentanglement by Latent Semantic Navigationcited by 8
  10. 2023VeRi3D: Generative Vertex-based Radiance Fields for 3D Controllable Human Image Synthesiscited by 8
  11. 2023WildFusion: Learning 3D-Aware Latent Diffusion Models in View Spacecited by 8
  12. 2024FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Objectcited by 6
6 more
  1. 2022AE-NeRF: Auto-Encoding Neural Radiance Fields for 3D-Aware Object Manipulationcited by 4
  2. 2024Learning 3D-Aware GANs from Unposed Images with Template Feature Fieldcited by 3
  3. 2023Class-Continuous Conditional Generative Neural Radiance Fieldcited by 1
  4. 2025SemFaceEdit: Semantic Face Editing on Generative Radiance Manifoldscited by 1
  5. 2023ZIGNeRF: Zero-shot 3D Scene Representation with Invertible Generative Neural Radiance Fieldscited by 0
  6. 2024Probabilistic Directed Distance Fields for Ray-Based Shape Representationscited by 0