pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis (2012.00926v2)

Abstract: We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks ($\pi$-GAN or pi-GAN), for high-quality 3D-aware image synthesis. $\pi$-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent 3D representations with fine detail. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.

• The paper introduces pi-GAN, a novel 3D-aware image synthesis model leveraging SIREN-based implicit radiance fields to capture fine details.
• It employs FiLM conditioning and progressive growing strategies to ensure multi-view consistency and efficient high-resolution training.
• Experimental results demonstrate superior performance with lower FID scores and improved image quality compared to models like HoloGAN and GRAF.

pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

The paper "pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis" presents a pioneering approach in the domain of generative models and neural rendering. This paper addresses critical limitations in existing 3D-aware image synthesis methods and proposes a novel model that leverages neural representations with periodic activation functions. The proposed model, referred to as pi-GAN, aims to overcome challenges pertaining to multi-view consistency and image quality that have hindered prior methods.

Key Contributions

The pi-GAN model introduces several innovations:

1. SIREN-Based Implicit Radiance Fields: The core representation network in pi-GAN utilizes Sinusoidal Representation Networks (SIREN), which employ periodic activation functions. This organization enables the effective encoding of fine details, which has been a significant limitation in previous models using ReLU-based representations.
2. Feature-Wise Linear Modulation (FiLM): Traditional conditioning-by-concatenation approaches fail to realize the potential of SIREN architectures. The authors propose a more effective conditioning method utilizing a mapping network that modulates each layer via FiLM. This technique enhances the expressiveness of the implicit neural representations.
3. Progressive Growing Strategy: Emulating the method in ProgressiveGAN, the pi-GAN introduces a progressive growing strategy to mitigate the computational load and complexity associated with high-resolution 3D image synthesis. This method accelerates training while maintaining convergence stability.

Proposed Methodology

The pi-GAN generator produces an implicit radiance field conditioned on a latent vector. This radiance field is then rendered using a differentiable volume rendering approach. Key method details include:

• SIREN Backbone: The generator leverages an MLP architecture with periodic activation functions (SIREN). The generator transforms a 3D location and 2D viewing direction into a view-dependent radiance and view-independent volume density, enabling view-consistent image rendering.
• Neural Volume Rendering: A radiance field is rendered from arbitrary camera poses using neural volume rendering, derived from classical volume rendering techniques. This allows explicit control over camera attributes, such as pose and focal length.

Experimental Results

The authors conducted rigorous experiments across several datasets, including CelebA, Cats, and CARLA. The results demonstrated that pi-GAN significantly outperforms contemporaneous methods such as HoloGAN and GRAF in terms of image quality and view consistency. Key findings include:

• Quantitative Metrics: The pi-GAN shows superior performance in standard evaluation metrics, with an FID score of 14.7 on CelebA, outperforming HoloGAN (39.7) and GRAF (41.1).
• Qualitative Consistency: The images synthesized by pi-GAN exhibit superior detail and multi-view consistency, effectively capturing fine features such as individual teeth and whiskers, which other models failed to render accurately.
• 3D Structure and Novel View Synthesis: The implicit radiance fields generated by pi-GAN allow for the extraction of proxy 3D shapes and the synthesis of novel views from unseen angles, illustrating the robustness of the underlying 3D-aware representations.

Implications and Future Work

The introduction of pi-GAN has substantial practical and theoretical implications:

• Improved Representation Learning: By employing periodic activation functions and FiLM conditioning, pi-GAN exemplifies a significant step forward in the representation learning of neural radiance fields for generative tasks.
• Applications in Graphics and Vision: The ability to synthesize high-quality, view-consistent images has numerous applications in computer graphics, vision, and related fields, such as augmented reality and content creation.
• Potential for Deepfake Detection: The capability of the model to generate realistic images may warrant the development of robust detection methodologies to prevent misuse in creating deceptive content.

Looking forward, several avenues can be explored to enhance pi-GAN:

• Efficiency Improvements: Reducing the computational demands of the neural rendering process could allow the generation of higher-resolution images.
• Enhanced 3D Shape Extraction: Strategies to refine the quality of 3D shape proxies extracted from radiance fields may open new applications in 3D modeling and animation.
• Bias and Ethical Considerations: Addressing the implicit bias in dataset distributions and ensuring the ethical use of such generative models remains a critical concern for future research.

In conclusion, the pi-GAN framework presents an innovative and effective approach to 3D-aware image synthesis, combining high image quality with robust multi-view consistency and providing a solid foundation for future advancements in neural rendering and generative modeling.