GaussianDreamer, the faster method for generating 3D models from text, is now being introduced.


A New Frontier in 3D Model Generation: Exploring the Possibilities of Text to 3D

We live in a world where AI-based generative content has become increasingly sophisticated and prevalent. From “text to image” to “image to image” or even “language to language”, the possibilities seem endless. However, there is one exciting concept that has caught the attention of many: “text to 3D”. Imagine being able to simply describe a 3D model in text and have the system generate it for you. This could revolutionize the consumer space, as easy access to 3D models has long been a barrier to widespread adoption of 3D printing.

Traditionally, consumers have struggled to create their own designs using complex CAD tools or navigate through online 3D model repositories, where finding the right model can be a daunting task. However, with the development of “text to 3D” systems, this could all change. Manufacturers of 3D printers would be able to focus on the massive consumer market if users could simply request a 3D model and have it generated for them.

Over the past few months, I have been closely following the progress of text to 3D systems. While initial attempts have been promising, the models generated have been rough and the systems limited to specific types of models. However, a new approach called “GaussianDreamer” might just change the game.

The researchers behind GaussianDreamer recognized the shortcomings of previous methods and decided to combine two AI approaches to tackle the problem. Their approach involves using a 3D diffusion model to provide point cloud priors for initialization and a 2D diffusion model to enhance the geometry and appearance. It’s an AI-on-AI approach that has shown immense success in other tools like ChatGPT.

The first stage of the GaussianDreamer process creates a basic 3D model, which is then refined using the innovative Gaussian Splatter technique. The researchers claim that their software, running on a single GPU, can generate a “high-quality” 3D model in just 25 minutes. And if multiple GPUs are used in parallel, the time required would be even shorter, with results achievable in less than a minute using 32 GPUs.

The provided samples in their research paper are truly impressive, showcasing the potential of this new approach. However, it’s worth noting that the samples are all single objects, which may be limiting in the realm of 2D images where complex scenes can be created. Nevertheless, when it comes to 3D printing, the ability to generate single objects quickly and efficiently is highly desirable.

Although this technology is still in the research stage, it holds immense promise for the future. Whether it will be commercialized remains uncertain. However, the researchers have made their software available on GitHub for public exploration, albeit requiring hardware and programming skills to utilize.

In conclusion, “text to 3D” is a concept that could pave the way for efficient 3D model generation, opening up new possibilities for 3D printing in the consumer space. The development of GaussianDreamer, with its combination of two AI approaches, shows great potential for creating high-quality 3D models in a remarkably short amount of time. While we may still be a few steps away from widespread adoption, the journey towards a powerful text to 3D concept is well underway.

***Via Tayonrani and GitHub***

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