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Advancements in Text-to-Image Generation: Creating Articulated 3D Models from Text

Advancements in Text-to-Image Generation: Creating Articulated 3D Models from Text

Title: Enhancing Text-to-Image Generators for 3D Model Creation

The rapid growth of generative AI technology has led to incredible advancements in picture production. Techniques like DALL-E, Imagen, and Stable Diffusion have allowed for the creation of stunning images based on textual cues. Now, there is potential to extend this progress to 3D models. DreamFusion has recently demonstrated the ability of a text-to-image generator to produce high-quality 3D models. In this article, we explore how we can maximize the capabilities of a text-to-image generator to generate articulated models of various 3D objects.

Creating Statistical Models of 3D Objects:
Instead of focusing on creating a single 3D asset, our goal is to create a statistical model of an entire class of articulated 3D objects. For example, we want to create animatable 3D assets of cows, sheep, and horses that can be used in AR/VR, gaming, and content creation. To achieve this, we train a network that can predict an articulated 3D model from a single photograph of the object.

Using Synthetic Data:
In the past, reconstruction networks relied on real data. However, we propose using synthetic data generated using a 2D diffusion model, such as Stable Diffusion. Our team at the Visual Geometry Group at the University of Oxford introduces Farm3D, an addition to existing 3D generators like DreamFusion, RealFusion, and Make-a-video-3D. Farm3D allows for the creation of a diverse range of 3D assets, whether static or dynamic, from text or an image, in a much shorter timeframe.

Benefits of Using a 2D Picture Generator:
There are several benefits to using a 2D picture generator. Firstly, it produces accurate and pristine examples of the object category, which helps curate the training data and streamline learning. Secondly, it provides virtual views of each object instance, enhancing our understanding of the objects. Lastly, it eliminates the need for real data gathering, making the approach more adaptable.

Fast and Manipulable 3D Model Generation:
Our network can reconstruct an articulated 3D model in a matter of seconds, allowing for real-time manipulation, such as animation and relighting. Unlike fixed models, our approach is suitable for synthesis and analysis because it can generalize to actual pictures while only being trained on virtual input. This opens up possibilities for studying and conserving animal behaviors.

Technical Innovations in Farm3D:
Farm3D is built on two key technical innovations. Firstly, we demonstrate how to create a large training set of clean pictures of an object category using Stable Diffusion. Secondly, we extend the Score Distillation Sampling (SDS) loss to achieve synthetic multi-view supervision. This allows us to train a photo-geometric autoencoder, which can create new artificial views of the object and improve the model’s performance.

Evaluation and Future Prospects:
Farm3D has undergone qualitative evaluation, proving its ability to produce and repair 3D models. Additionally, it performs at least as well as various baselines, even though it doesn’t rely on real images for training. This saves time and effort in data gathering and curation. The potential applications of Farm3D are vast, from semantic key point transfer to creating new 3D assets.

The advancements in generative AI have opened up new possibilities for creating high-quality 3D models. By leveraging text-to-image generators and training networks with synthetic data, we can generate articulated 3D models of different object types. Farm3D is a significant step towards achieving this goal, offering fast and adaptable 3D model generation for various applications.

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