Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks
(2404.10625)Abstract
NeRF-based 3D-aware Generative Adversarial Networks (GANs) like EG3D or GIRAFFE have shown very high rendering quality under large representational variety. However, rendering with Neural Radiance Fields poses challenges for 3D applications: First, the significant computational demands of NeRF rendering preclude its use on low-power devices, such as mobiles and VR/AR headsets. Second, implicit representations based on neural networks are difficult to incorporate into explicit 3D scenes, such as VR environments or video games. 3D Gaussian Splatting (3DGS) overcomes these limitations by providing an explicit 3D representation that can be rendered efficiently at high frame rates. In this work, we present a novel approach that combines the high rendering quality of NeRF-based 3D-aware GANs with the flexibility and computational advantages of 3DGS. By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time. Additionally, our approach allows for a high resolution GAN inversion and real-time GAN editing with 3D Gaussian Splatting scenes.
Overview
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This paper introduces a novel methodology that merges 3D-aware Generative Adversarial Networks (GANs) with 3D Gaussian Splatting (3DGS) to offer an efficient and realistic rendering framework suitable for real-time applications like virtual reality and video games.
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It addresses the challenges in rendering Neural Radiance Field (NeRF)-based 3D GANs by presenting a decoder framework that translates implicit NeRF representations into explicit 3DGS scenes, allowing for real-time editing and high-quality rendering.
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The proposed decoder leverages geometric information from pre-trained GANs for accurate Gaussian splat positioning and offers significant improvements in rendering speeds, resolution, and aspect ratios without the need for superresolution modules.
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Experimental evaluations show that this approach generates high-fidelity 3D models up to 5x faster than traditional methods, providing a substantial contribution to the fields of computer graphics and vision by enabling the practical use of high-fidelity 3D models in real-time applications.
Integrating 3D-aware GANs with 3D Gaussian Splatting for Efficient and Realistic Rendering
Introduction
The adaptation of generative adversarial networks (GANs) for three-dimensional (3D) content generation has marked a significant leap in the field of computer graphics and vision, particularly for applications demanding the creation and editing of 3D assets such as in virtual reality (VR) or video games. This paper addresses the computational and integration challenges associated with rendering Neural Radiance Field (NeRF)-based 3D-aware GANs, like Efficient Geometry-aware 3D GAN (EG3D) and GIRAFFE, in real-time applications. It introduces a novel methodology combining the high rendering quality of NeRF-based 3D GANs with the computational efficiency and flexibility of 3D Gaussian Splatting (3DGS), thereby presenting an efficient decoder framework capable of translating implicit NeRF representations into explicit and editable 3D Gaussian Splatting attributes.
Related Work
The convergence of advancements in NeRF and 3DGS presents a pivotal foundation for this research. NeRF’s implicit scene representation offers highly detailed and flexible novel view synthesis but at a cost of significant computational resources for training and inference. Meanwhile, 3DGS proposes a move toward explicit scene representation through the use of Gaussian splats, achieving notable improvements in rendering speeds without compromising on image quality. The application of 3D-aware GANs for content synthesis has been propelled by such advancements, but their direct application in real-time 3D environments remained cumbersome due to inherent limitations in modifying the generated content post-synthesis and the intensive computational demands.
Methodology
The core contribution is a novel decoder that maps latent representations from 3D-aware GANs to the explicit 3D Gaussian Splatting scenes, facilitating real-time editing and rendering of high-quality 3D models. This approach circumvents the need for superresolution modules by rendering the scenes directly at high resolutions. Key innovations include:
- Position Initialization: A technique leveraging the geometric information in the pre-trained GAN’s tri-plane for accurate Gaussian splat positioning, crucial for the fidelity of re-rendered scenes.
- Decoder Architecture: Sequential architecture for the decoder network is designed for the efficient sampling of Gaussian splat attributes from tri-plane features, fostering a dependency chain for attribute determination which enhances the realism of generated scenes.
- Backbone Fine-tuning: Adapting the generator backbone of the 3D-aware GAN during training refines the latent space representations for better compatibility with 3DGS, addressing the geometric and visual attributes more effectively.
Experiments and Results
Experimental validation demonstrates the decoder’s ability to generate 3D models with high fidelity, comparable to their NeRF-based counterparts, alongside offering significant improvements in rendering speeds and flexibility in terms of resolution and aspect ratio adjustments. The framework was tested against several 3D-aware GANs, including EG3D and PanoHead, showing a remarkable increase in rendering speed (up to 5x faster) without compromising image quality. A comprehensive set of metrics including MSE, LPIPS, SSIM, and ID Similarity were utilized for quantitative analysis, supplemented by qualitative evaluations showcasing nearly indistinguishable comparisons between original GAN outputs and decoded 3DGS renderings.
Discussion and Future Directions
This research lays a foundational paradigm for integrating latent space representation of complex 3D scenes with efficient rendering techniques, highlighting a path toward their practical application in real-time and resource-constrained environments. The findings point toward an emergent realm where high-quality 3D content generation becomes more accessible across domains, from gaming and entertainment to simulations and educational content development.
Future work may explore end-to-end training mechanisms for jointly optimizing GAN and decoder performance, enhance the model to encompass wider representation varieties beyond human heads, and potentially integrate view-dependent rendering capabilities to further improve the realism of synthesized 3D models.
Conclusion
The introduced method skillfully bridges a significant gap in the field of 3D content generation by melding the representational richness of 3D-aware GANs with the operational efficiency of 3D Gaussian Splatting. This innovative approach not only paves the way for the practical use of high-fidelity 3D models in real-time applications but also sets a precedent for future explorations in the effective synthesis and rendition of 3D content.
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