Advances in Text-to-3D Content Generation: Techniques and Applications

In recent years, there has been significant progress in the field of text-to-3D content generation, with advancements driven by CLIP-based guidance or score distillation. These methods utilize pre-trained text-to-image diffusion models as a foundation, enabling the creation of diverse and imaginative 3D content. Despite these developments, a prevailing limitation exists in the use of RGB data, which often results in models with inherent lighting and shadows effects that detract from their realism. To address this gap, recent methods have been exploring the generation of 3D assets from textual descriptions under the supervision of 2D diffusion models.
To demystify complex concepts, let’s consider the following analogies:

• Think of CLIP Score as a "fitness level" for measuring how well a method can convert text to 3D. Just like athletes compete in different sports, these methods are compared based on their ability to generate high-quality 3D content from textual descriptions.
• CLIP R-Precision can be likened to a "racing time." It measures how accurately the generated 3D object matches the original text description, with faster times indicating better performance.
• User studies provide valuable feedback on the quality and realism of the generated 3D content, much like a chef tasting their dish and providing constructive criticism.

Now, let’s delve into the main points of the article

1. Recent advancements in text-to-3D generation have made it possible to create imaginative and well-geometrical 3D objects from textual descriptions.
2. Despite these developments, a limitation exists in the use of RGB data, which can result in models with inherent lighting and shadows effects that detract from their realism.
3. To address this gap, recent methods have been exploring the generation of 3D assets from textual descriptions under the supervision of 2D diffusion models.
4. These methods achieve impressive results, but they cannot generate relightable objects, as they typically represent the underlying illumination and texture of an object as a holistic appearance.
5. To enhance generation quality, subsequent studies have diversified the pipeline, focusing on aspects like 3D representations, loss functions, 3D priors, and 2D diffusion models.
6. Although these methods achieve impressive results, they still face challenges in generating relightable objects that can accurately represent their underlying illumination and texture.
   In conclusion, the article provides a comprehensive review of recent advancements in text-to-3D content generation, highlighting the limitations and challenges that remain to be addressed. By leveraging the power of CLIP-based guidance or score distillation, these methods have shown promising results in creating diverse and imaginative 3D content from textual descriptions. However, further research is needed to overcome the remaining challenges and generate relightable objects that can accurately represent their underlying illumination and texture.