Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation

In this talk, I will present Geometry Image Diffusion (GIMDiffusion), a novel method designed to generate 3D objects from text prompts efficiently. GIMDiffusion uses geometry images, a 2D representation of 3D shapes, which allows the use of existing image-based architectures instead of complex 3D-aware models. This approach reduces computational costs and simplifies the model design. By incorporating Collaborative Control, the method exploits rich priors of pretrained Text-to-Image models like Stable Diffusion, enabling strong generalization even with limited 3D training data. GIMDiffusion produces 3D objects with semantically meaningful, separable parts and internal structures, which enhances the ease of manipulation and editing.