"Objaverse-XL: A New Paradigm for 3D Object Modeling for AI and Robotics
A Generative Dataset for text to 3D Object Modeling
3D models are really useful.
As we have learned in previous posts, 3D objects guide Microsoft’s agent development, help Google and Toyota train their robots, and are a perquisite for immersive gaming.
One of the datasets can be used to such training is Objaverse.
Objaverse-XL, Allen Institute for Artificial Intelligence most recent publicly available dataset, marks a big leap forward in the development of 3D generative models. With its unprecedented scale and diversity, I think Objaverse-XL lays the groundwork to revolutionize the way we interact with and understand the world around us.
Might be hyperbole though. Access to the datasets and code after the paywall.
A Dataset of Unparalleled Scale (that’s the pun)
At the heart of Objaverse-XL are over 10 million 3D objects, that’s about 12 times larger than Objaverse 1.0. In my mind, this makes it one of the largest and most diverse datasets of its kind allows for a level of diversity and complexity previously unseen before.
This massive collection of objects spans a wide range of categories, encompassing everything from everyday household items to intricate architectural structures provide a great foundation for training generative models that can capture the nuances of the physical world with extraordinary fidelity.
Unlocking New Frontiers in 3D Generative Modeling
The objects in Objaverse-XL range from manually designed items to photogrammetry scans of landmarks and everyday items, as well as professional scans of historic and antique artifacts. This wide variety of objects allows for a comprehensive understanding of 3D objects and their properties.
This implies that by providing a comprehensive representation of the 3D world, Objaverse-XL unlocks generative models that perform a wide range of tasks, including:
3D Object Generation: Models can learn to generate entirely new 3D objects, fostering creativity and enabling the design of novel products and structures.
Image-to-3D Generation: Models can transform 2D images into realistic 3D representations, bridging the gap between the visual and physical worlds.
3D Rendering: Models can enhance the realism of 3D renderings, making them indistinguishable from real-world objects.
Read Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets for more on this. Paper review is in my queue.
The Intersection of Objaverse-XL and Autonomous Agents
The billion dollar and obvious use case for training agents with Objaverse-XL is in the field of robotics. The hypothesis is that a robot trained on this dataset could learn to navigate complex indoor environments, manipulate objects, and interact with humans in a more natural and intuitive way.
Another potential use case is in the field of gaming and extended reality (XR). Autonomous agents trained on Objaverse-XL could be used to populate virtual environments, providing a more realistic and immersive experience for users.
Just have a look at this only two-year-old “GAN Theft Auto” video. OpenAI’s Sora can soon do the same thing.
Playing a Neural Network's version of GTA V: GAN Theft Auto - YouTube
Traditionally, training an autonomous agent would traditionally require a reinforcement learning algorithm, which involves training the agent to make decisions based on rewards and punishments.
The agent would be presented with a series of 3D environments from the dataset and would need to learn to perform tasks such as navigating the environment, manipulating objects, and interacting with other agents. The problem here is though that RL doesn’t generalize well outside of the video game the agent is trained on. I.e., a Super Mario RL agent can’t play Chess. If you want to have a look at my Tic Tac Toe RL bot on GitHub see links below after the paywall.
Now in addition to operating within a game, it could soon be possible to generate full 3D worlds through generative AI. That would totally redefine indy-gaming.
The intersection of Objaverse-XL and autonomous agents in gaming and robotics is a fascinating area of research. By leveraging the Objaverse-XL dataset, autonomous agents can gain a deeper understanding of the 3D world, allowing them to interact with it in more meaningful ways.
In Conclusion — A Catalyst for Innovation
In my opinion. using Objaverse datasets to train generative models is poised to ignite a wave of innovation in various industries, including:
Product Design: Designers can utilize generative models to rapidly create and iterate on new product designs, accelerating the design process and enhancing creativity.
Virtual Reality and Augmented Reality: Generative models can power the creation of immersive virtual worlds and realistic augmented reality experiences.
Robotics: Generative models can enable robots to perceive and interact with the physical world more effectively, expanding their capabilities and applications.
As generative models continue to evolve, we can anticipate even more groundbreaking applications that will shape the future of technology and our interactions with the physical world.
Clearly, I will continue to explore this intersection throughout this publication, you can expect to see more posts on new ideas, breakthroughs and innovative applications.
Exciting times. Stay tuned.
Saved you a click