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Despite the latest remarkable advances in generative modeling, efficientgeneration of high-quality 3D assets from textual prompts remains a difficulttask. A key challenge lies in data scarcity: the most extensive 3D datasetsencompass merely millions of assets, while their 2D counterparts containbillions of text-image pairs. To address this, we propose a novel approachwhich harnesses the power of large, pretrained 2D diffusion models. Morespecifically, our approach, HexaGen3D, fine-tunes a pretrained text-to-imagemodel to jointly predict 6 orthographic projections and the correspondinglatent triplane. We then decode these latents to generate a textured mesh.HexaGen3D does not require per-sample optimization, and can infer high-qualityand diverse objects from textual prompts in 7 seconds, offering significantlybetter quality-to-latency trade-offs when comparing to existing approaches.Furthermore, HexaGen3D demonstrates strong generalization to new objects orcompositions.

HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation