GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images (2209.11163v1)

Abstract: As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performant 3D generative models that synthesize textured meshes which can be directly consumed by 3D rendering engines, thus immediately usable in downstream applications. Prior works on 3D generative modeling either lack geometric details, are limited in the mesh topology they can produce, typically do not support textures, or utilize neural renderers in the synthesis process, which makes their use in common 3D software non-trivial. In this work, we introduce GET3D, a Generative model that directly generates Explicit Textured 3D meshes with complex topology, rich geometric details, and high-fidelity textures. We bridge recent success in the differentiable surface modeling, differentiable rendering as well as 2D Generative Adversarial Networks to train our model from 2D image collections. GET3D is able to generate high-quality 3D textured meshes, ranging from cars, chairs, animals, motorbikes and human characters to buildings, achieving significant improvements over previous methods.

• The paper's main contribution is leveraging differentiable surface extraction and rendering to produce direct 3D textured meshes from 2D images.
• It integrates 2D GAN architectures with 3D modeling to achieve complex geometry and detailed textures that surpass previous methods.
• Experimental results show improved coverage and accuracy across categories like cars and chairs, highlighting practical applications in gaming, VR, and filmmaking.

An Overview of GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

The paper "GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images" introduces a novel approach to 3D generative modeling that addresses the limitations of previous methods concerning geometric details, texture support, and direct usability in conventional 3D software. The proposed model, referred to as GET3D, leverages advancements in differentiable surface modeling, rendering, and 2D Generative Adversarial Networks (GANs) to produce high-quality explicit 3D textured meshes from 2D images. The authors manage to bridge these technologies, allowing for the generation of complex 3D shapes that can include intricate topologies and detailed, realistic textures.

Key Contributions

The primary contribution of this work lies in the ability of GET3D to generate explicit textured 3D meshes directly usable by standard graphics engines. The model achieves this via:

1. Differentiable Surface Extraction: GET3D uses a differentiable surface extraction process that enables the model to learn and output 3D shapes with complex topology. This process ensures that the generated shapes can be directly manipulated within conventional graphics pipeline software.
2. Differentiable Rendering for Training: The introduction of a differentiable rendering layer allows the model to be trained using 2D images, effectively utilizing the wide availability of such data over explicit 3D data. This process is instrumental in aligning the network's understanding of 3D geometry and texture from available 2D projections, providing a more data-inclusive approach to training.
3. Integration with 2D GANs: By employing architectures akin to those in StyleGAN, GET3D allows the synthesis of high-resolution texture details that complement the geometric outputs. This makes the generated assets not only structurally complex but also visually compelling.

Numerical Results and Claims

The results presented in the paper indicate a notable improvement in generating 3D shapes over prior methods across several categories, including cars, chairs, motorbikes, and more. The metrics reported show high coverage (COV) and lower minimum matching distances (MMD), suggesting both a diverse and accurate generation of 3D models. Additionally, in qualitative assessments, GET3D generates visible improvements in terms of texture detail and geometry complexity compared to state-of-the-art alternatives like PiGAN and EG3D.

Implications and Future Developments in AI

The practical implications of this research are broad, influencing various fields that require large-scale 3D content, such as gaming, virtual reality, and filmmaking. By reducing the reliance on manual creation and facilitating automated generation, GET3D potentially alleviates bottlenecks associated with labor-intensive 3D modeling. Furthermore, the methodology propels future developments in AI towards more seamless integrations of 3D data with machine learning frameworks, likely accelerating the development of more generalized generative models capable of handling multifaceted tasks involving both 2D and 3D data.

On a theoretical level, the convergence of 3D surface modeling with GAN training frameworks offers fertile ground for further explorations into generative modeling's capabilities concerning high-dimensional outputs. Future research directions could include optimizing the model's performance with real-world datasets, which often contain noise and incomplete data, thereby highlighting a pathway toward robust 3D model generation from imperfect input data. Additionally, expanding the model's utility to encompass even more generalized categories, using diverse datasets, might lead to breakthroughs in cross-domain generative synthesis.

In summary, GET3D marks a significant stride in 3D generative modeling by effectively marrying sophisticated surface extraction techniques with advancements in neural rendering and GANs, offering both a practical tool for digital content creators and a theoretical advancement for the machine learning community.