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arXiv · 2401.07727

HexaGen3D: StableDiffusion is just 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
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Abstract

Despite the latest remarkable advances in generative modeling, efficient generation of high-quality 3D assets from textual prompts remains a difficult task. A key challenge lies in data scarcity: the most extensive 3D datasets encompass merely millions of assets, 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 latent triplane. 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 when comparing to existing approaches. Furthermore, HexaGen3D demonstrates strong generalization to new objects or compositions.

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Antoine Mercier, Ramin Nakhli, Mahesh Reddy, Rajeev Yasarla, Hong Cai, Fatih Porikli, Guillaume Berger. 2024-01-15. HexaGen3D: StableDiffusion is just one step away from Fast and Diverse Text-to-3D Generation. https://arxiv.org/abs/2401.07727

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