Text-to-X models have grown rapidly recently, with most of the advancement being in text-to-image models. These models can generate photorealistic images using the given text message.
Imaging is only one component of a broad research landscape in this field. While it is an important aspect, there are also other Text-to-X models that play a crucial role in different applications. For example, text-to-video models aim to generate realistic videos based on a given text message. These models can significantly speed up the content preparation process.
On the other hand, 3D text generation has become a critical technology in the fields of computer vision and graphics. Although still in its early stages, the ability to generate realistic 3D models from text input has attracted significant interest from both academic researchers and industry professionals. This technology has immense potential to revolutionize various industries, and experts in multiple disciplines are closely monitoring its continued development.
Neural Radiance Fields (NeRF) is a recently introduced approach that enables high-quality rendering of complex 3D scenes from a set of 2D images or a sparse set of 3D points. Several methods have been proposed to combine text-to-3D models with NeRF to obtain more pleasing 3D scenes. However, they often suffer from distortion and artifacts and are sensitive to text prompts and random seeds.
In particular, the problem of 3D inconsistency is a common problem in which rendered 3D scenes produce geometric features belonging to the front view multiple times in various viewpoints, resulting in strong distortions in the 3D scene. This glitch occurs due to the 2D diffusion model’s lack of awareness of 3D information, especially the camera pose.
What if there was a way to combine text-to-3D models with the NeRF trailer for realistic 3D renderings? time to meet 3DFuse.
3DFuse is an intermediate approach that combines a pretrained 2D diffusion model imbued with 3D awareness to make it suitable for consistent 3D NeRF optimization. It effectively injects 3D awareness into pre-trained 2D diffusion models.
3DFuse It starts with semantic code sampling to speed up the semantic identification of the generated scene. This semantic code is actually the generated image and given text message for the broadcast model. Once this step is done, the consistency injection module 3DFuse takes this semantic code and gets a viewpoint-specific depth map by projecting a coarse 3D geometry for the given viewpoint. They use an existing model to achieve this depth map. Then the depth map and the semantic code are used to inject 3D information into the diffusion model.
The problem here is that the predicted 3D geometry is prone to errors and that could alter the quality of the generated 3D model. Therefore, it must be handled before proceeding with the pipeline. To solve this problem, 3DFuse introduces a shallow depth injector that implicitly knows how to correct problematic depth information.
Distilling the diffusion model score that produces consistent 3D images, 3DFuse Stably optimizes NeRF for view-coherent text-to-3D generation. The framework achieves a significant improvement over previous works in generation quality and geometric consistency.
review the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 18k+ ML SubReddit, discord channeland electronic newsletterwhere we share the latest AI research news, exciting AI projects, and more.
🚀 Check out 100 AI tools at AI Tools Club
Ekrem Çetinkaya received his B.Sc. in 2018, and M.Sc. in 2019 from Ozyegin University, Istanbul, Türkiye. She wrote her M.Sc. thesis on denoising images using deep convolutional networks. She received her Ph.D. He graduated in 2023 from the University of Klagenfurt, Austria, with his dissertation titled “Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning.” His research interests include deep learning, computer vision, video encoding, and multimedia networking.
