GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis (2007.02442v4)

Abstract: While 2D generative adversarial networks have enabled high-resolution image synthesis, they largely lack an understanding of the 3D world and the image formation process. Thus, they do not provide precise control over camera viewpoint or object pose. To address this problem, several recent approaches leverage intermediate voxel-based representations in combination with differentiable rendering. However, existing methods either produce low image resolution or fall short in disentangling camera and scene properties, e.g., the object identity may vary with the viewpoint. In this paper, we propose a generative model for radiance fields which have recently proven successful for novel view synthesis of a single scene. In contrast to voxel-based representations, radiance fields are not confined to a coarse discretization of the 3D space, yet allow for disentangling camera and scene properties while degrading gracefully in the presence of reconstruction ambiguity. By introducing a multi-scale patch-based discriminator, we demonstrate synthesis of high-resolution images while training our model from unposed 2D images alone. We systematically analyze our approach on several challenging synthetic and real-world datasets. Our experiments reveal that radiance fields are a powerful representation for generative image synthesis, leading to 3D consistent models that render with high fidelity.

• The paper introduces GRAF, a model that leverages radiance fields to enable high-resolution 3D-aware image synthesis from unposed 2D images.
• It employs a patch-based multi-scale discriminator to disentangle camera and scene properties, ensuring multi-view consistency and improved fidelity.
• Empirical evaluations show GRAF outperforms state-of-the-art methods in FID and 3D consistency on both synthetic and real-world datasets.

Generative Radiance Fields for 3D-Aware Image Synthesis

Introduction and Motivation

The paper "Generative Radiance Fields for 3D-Aware Image Synthesis" addresses a noteworthy limitation in the field of 2D generative adversarial networks (GANs): their inability to understand and generate the intricacies of 3D scenes from 2D images. Current state-of-the-art 2D GANs fall short in disentangling camera and scene properties, often entangling object identity with viewpoint, which compromises image fidelity and multi-view consistency. This paper proposes GRAF, a novel generative model leveraging radiance fields for high-resolution 3D-aware image synthesis, which efficiently disentangles these factors.

Problem Statement and Contributions

Traditional approaches to 3D-aware image synthesis include voxel-based methods and the use of intermediate 3D feature representations combined with differentiable rendering. However, these methods often yield low-resolution images or suffer from entangled latent representations. The paper posits that radiance fields, with their continuous nature, offer a more promising solution. Specifically, the contributions of this work are:

1. Generative Neural Radiance Fields (GRAF): A generative model for 3D-aware image synthesis that can generate high-resolution images from unposed 2D images.
2. Patch-based Multi-scale Discriminator: A crucial component allowing efficient learning of high-resolution generative radiance fields.
3. Systematic Evaluation: Comprehensive testing on synthetic and real-world datasets, showing favorable comparisons to existing state-of-the-art methods in terms of visual fidelity and 3D consistency.

Methodology

Neural Radiance Fields (NeRF)

Neural radiance fields model a scene as a continuous function that maps a 3D location and viewing direction to an RGB color value and a volume density. They enable novel view synthesis by approximating the volumetric projection integral through numerical integration.

Generative Radiance Fields (GRAF)

GRAF extends the NeRF framework to generative modeling by:

1. Generator:
   • Takes camera matrix $K$, camera pose $\Theta$, 2D sampling pattern $\pi$, and shape/appearance latent codes $z_s$ and $z_a$.
   • Predicts an image patch using the radiance field conditioned on these inputs.
2. Discriminator:
   • Compares real and synthesized patches extracted from real images using bilinear sampling.
   • Utilizes a convolutional neural network trained with multi-scale patch sampling, ensuring a consistent receptive field and capturing both global and local image features.

Implementation Details

• Ray Sampling: For numerical integration of the radiance fields, 3D points along rays are sampled.
• Conditional Radiance Field: A deep neural network maps positional encodings of 3D points and viewing directions to RGB color values and volume densities, conditioned on shape and appearance codes (latent variables).
• Volume Rendering: Color values for each pixel are computed using the volume rendering operator.

Empirical Evaluation

The method was systematically evaluated on several datasets, including synthetic images of chairs and cars, and real-world images from CelebA, CelebA-HQ, Cats, and Birds datasets. The evaluation metrics used include Frechet Inception Distance (FID) and Kernel Inception Distance (KID), complemented by qualitative multi-view consistency analysis using COLMAP.

Results

• Image Fidelity: GRAF outperformed several state-of-the-art methods like PLATONIC GAN and HoloGAN in terms of FID, especially at high resolutions.
• 3D Consistency: The approach demonstrated superior multi-view consistency, validated through dense 3D reconstructions.
• Scalability: The method generalizes well to higher resolutions, improving performance when trained and sampled at full resolution.

Discussion

The findings highlight the potential of radiance fields over voxel-based methods for 3D-aware image synthesis. The proposed approach also effectively disentangles shape and appearance, allowing for independent manipulation during inference, which is a significant advantage over traditional methods that often entangle these properties.

Future Directions

While the results are promising, the current approach is limited to single objects within simple scenes. Future research may focus on incorporating inductive biases such as depth maps or symmetry to extend the model's capabilities to more complex, real-world scenarios. Further development in this area could enable applications in virtual reality, data augmentation, and robotics, enhancing training and validation through cost-efficient, photorealistic, and large-scale 3D models.

Implications and Broader Impact

The advancements in 3D-aware image synthesis have far-reaching implications for various domains. However, ethical considerations, such as the potential misuse of realistic generative models to create misleading content, must be addressed. It is imperative to balance innovation with the development of methods to distinguish between synthetic and real-world content to mitigate risks associated with the proliferation of deep fakes.

Conclusion

The paper makes a significant contribution to the field of 3D-aware image synthesis by proposing GRAF, which leverages generative neural radiance fields to achieve high-resolution, multi-view consistent image synthesis. This work opens new avenues for practical applications and sets the stage for future explorations in generating complex 3D-aware scenes.