title: HexaGen3D: StableDiffusion is just one step away from Fast and Diverse
    Text-to-3D Generation
summary: 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.
paper_authors._id: 65a76e840f169670d78e92a8
paper_authors.name: Antoine Mercier
paper_authors._id: 65a76e840f169670d78e92a9
paper_authors.name: Ramin Nakhli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-17T10:09:53.945Z
paper_authors._id: 613a36a1a488863c19207314
paper_authors.avatarUrl: /avatars/63520d7e81b08dc0ca468f73047117d6.svg
paper_authors.fullname: Ramin Nakhli
paper_authors.type: user
paper_authors.user: raminnakhli
paper_authors._id: 65a76e840f169670d78e92aa
paper_authors.name: Mahesh Reddy
paper_authors._id: 65a76e840f169670d78e92ab
paper_authors.name: Rajeev Yasarla
paper_authors._id: 65a76e840f169670d78e92ac
paper_authors.name: Hong Cai
paper_authors._id: 65a76e840f169670d78e92ad
paper_authors.name: Fatih Porikli
paper_authors._id: 65a76e840f169670d78e92ae
paper_authors.name: Guillaume Berger
paper_title: HexaGen3D: StableDiffusion is just one step away from Fast and Diverse
    Text-to-3D Generation
paper_summary: 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.
paper_ai_summary: HexaGen3D fine-tunes a 2D diffusion model to generate high-quality 3D assets from textual prompts using 6 orthographic projections and a latent triplane, offering better quality-to-latency trade-offs than existing methods.
paper_ai_keywords: diffusion models text-to-image orthographic projections latent triplane textured mesh per-sample optimization