HexaGen3D: StableDiffusion is One Step Away from Fast and Diverse Text-to-3D Generation

Antoine Mercier, Ramin Nakhli, Mahesh Reddy, Rajeev Yasarla, Hong Cai, Fatih Porikli, Guillaume Berger; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1247-1257

Despite the latest remarkable advances in generative modeling efficient generation of high-quality 3D objects from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass merely millions of samples while their 2D counterparts contain billions of text-image pairs. To address this we propose a novel approach which harnesses the power of large pretrained 2D diffusion models. More specifically our approach HexaGen3D fine-tunes a pretrained text-to-image model to jointly predict 6 orthographic projections and the corresponding 3D latent. We then decode these latents to generate a textured mesh. HexaGen3D does not require per-sample optimization and can infer high-quality and diverse objects from textual prompts in 7 seconds offering significantly better quality-to-latency trade-offs than existing approaches. Furthermore HexaGen3D demonstrates strong generalization to new objects or compositions.