Introduction to Generative 3D [full course, hands-on]

In this full course on Generative 3D, Dylan explores the intersection of machine learning and 3D technology, emphasizing the importance of understanding 3D in AI for achieving general intelligence. He covers various 3D representations like meshes, splats, and Gaussian splatting, guiding participants through setting up generative 3D demos and showcasing practical applications in industries such as games, movies, and retail.

In this full course on Generative 3D, Dylan, also known as individual KS, provides an in-depth look at the intersection of machine learning and 3D technology. He starts by explaining the current state of 3D in the machine learning domain, highlighting the rapid advancements and new research in the field. Dylan emphasizes the importance of understanding 3D in AI, especially for achieving general intelligence and grounding AI in the 3D world. The course aims to demystify the complexities of 3D representations and guide participants through setting up their own generative 3D demos.

The course covers the significance of 3D applications in various industries such as games, movies, and retail, showcasing the practical implications of machine learning in creating 3D content. Dylan introduces different 3D representations like meshes, splats, and Nerfs, explaining their role in machine learning models. He discusses the challenges in mesh representation and the transition towards non-mesh representations like splats that can be rendered in real-time, offering a more AI-friendly approach to 3D content generation.

The course delves into multiview diffusion, a critical component in generative 3D pipelines, explaining its role in generating multiple views of objects from different perspectives. Dylan walks participants through setting up a multiview diffusion demo using Google Colab and Hugging Face tools, showcasing how to train and deploy models efficiently. He highlights the importance of the hugging face ecosystem in staying updated with the latest developments in generative 3D technology.

Dylan explores Gaussian splatting as a differentiable rasterization technique that enables rendering ml-friendly 3D content in real-time. He explains the concept of splats composed of points with various parameters, illustrating how they can be efficiently rasterized using this technique. The course provides hands-on experience in setting up a Gaussian splatting demo, demonstrating how to generate and render splat data for 3D visualization. Participants are encouraged to explore the hugging face ecosystem for open-source tools to stay abreast of advancements in generative 3D technology.

The course concludes with a discussion on meshes and the challenges associated with traditional mesh generation algorithms like marching cubes. Dylan introduces innovative solutions such as mesh simplification techniques used in projects like Mesh Anything to convert dense meshes to clean, low-poly structures. Participants are guided through the process of building their own generative 3D demo using an LGN-based pipeline, hosting their models and spaces for image-to-mesh or image-to-splat transformations. Upon completion of the course, participants are encouraged to continue exploring open-source projects like Instant Mesh to stay updated on the latest developments in the rapidly evolving field of 3D machine learning.