CAD: Photorealistic 3D Generation via Adversarial Distillation (2312.06663v1)

Abstract: The increased demand for 3D data in AR/VR, robotics and gaming applications, gave rise to powerful generative pipelines capable of synthesizing high-quality 3D objects. Most of these models rely on the Score Distillation Sampling (SDS) algorithm to optimize a 3D representation such that the rendered image maintains a high likelihood as evaluated by a pre-trained diffusion model. However, finding a correct mode in the high-dimensional distribution produced by the diffusion model is challenging and often leads to issues such as over-saturation, over-smoothing, and Janus-like artifacts. In this paper, we propose a novel learning paradigm for 3D synthesis that utilizes pre-trained diffusion models. Instead of focusing on mode-seeking, our method directly models the distribution discrepancy between multi-view renderings and diffusion priors in an adversarial manner, which unlocks the generation of high-fidelity and photorealistic 3D content, conditioned on a single image and prompt. Moreover, by harnessing the latent space of GANs and expressive diffusion model priors, our method facilitates a wide variety of 3D applications including single-view reconstruction, high diversity generation and continuous 3D interpolation in the open domain. The experiments demonstrate the superiority of our pipeline compared to previous works in terms of generation quality and diversity.

• The paper introduces a novel adversarial distillation method that leverages diffusion models and 3D GANs to produce high-fidelity 3D representations.
• It employs triplane feature generation with latent codes from a Gaussian distribution to ensure continuous, diverse sampling and multi-view consistency.
• Strategies such as pose pruning and distribution refinement are used to mitigate overfitting and enhance sample quality across varied viewpoints.

Introduction to 3D Generation with AI

3D content generation has become increasingly valuable in various industries, including gaming, animation, and augmented reality. Traditionally, creating high-quality 3D models has been a time-consuming process, requiring significant manual labor. The advent of AI and machine learning, particularly generative models, provides a solution to automate and streamline this task. In this blog post, we'll delve into an innovative method that uses pre-trained diffusion models to generate photorealistic 3D content based on a single input image and a descriptive text prompt.

Understanding the Method

The method discussed here presents a novel learning paradigm for 3D synthesis, leveraging the strengths of diffusion models and Generative Adversarial Networks (GANs). Instead of seeking out specific modes or configurations as previous models did – which could lead to saturated colors, overly smooth features, or distorted, Janus-faced artifacts – this approach focuses on modeling the distribution of data in a more adversarial manner. This allows the generation of high-fidelity 3D content while avoiding common pitfalls of older models.

Breakthrough in 3D Content Creation

The crux of this approach lies in how the 3D generator is trained. A generator network receives a latent code drawn from a standard Gaussian distribution and produces triplane feature representations. In practice, this means that the generator models a continuous distribution, which inherently resolves mode-seeking issues that previous methods faced. Furthermore, it's capable of accommodating various downstream applications, including diversified sampling, single-view reconstruction, and continuous 3D interpolation.

Tackling Technical Challenges

One of the significant challenges in assimilating knowledge from pre-trained 2D diffusion models into a 3D GAN is avoiding overfitting to particular viewpoints and ensuring multi-view consistency. To confront these issues, novel strategies such as pose pruning and distribution refinement are introduced. Pose pruning filters out problematic viewpoints, ensuring geometric and semantic consistency. Meanwhile, distribution refinement strategies enhance the quality and diversity of the samples, leading to more visually appealing and varied outputs.

Evaluation and Results

The model was tested on various datasets to evaluate its capabilities. It excels in generating photorealistic and diverse 3D objects, conditioned on a single reference image along with a text description. Compared to previous works, this method outperforms in producing high-quality and consistent renderings across different viewpoints.

Conclusion

The described method opens up new possibilities for 3D content generation, offering an efficient and scalable solution. By merging the world of 2D diffusion models with 3D GANs, it paves the way for high-volume production of photorealistic 3D models with nuanced textures and details that closely follow user-provided prompts, transforming the field of automated 3D creation.