Bolt3D: Generating 3D Scenes in Seconds

Stanislaw Szymanowicz, Jason Y. Zhang, Pratul Srinivasan, Ruiqi Gao, Arthur Brussee +4 more
3/18/2025
cs.CV

No official code found. Best implementation: ZiYang-xie/WorldGen (2,144 stars, Apache-2.0, last commit 2026-04-12).

Abstract

We present a latent diffusion model for fast feed-forward 3D scene generation. Given one or more images, our model Bolt3D directly samples a 3D scene representation in less than seven seconds on a single GPU. We achieve this by leveraging powerful and scalable existing 2D diffusion network architectures to produce consistent high-fidelity 3D scene representations. To train this model, we create a large-scale multiview-consistent dataset of 3D geometry and appearance by applying state-of-the-art dense 3D reconstruction techniques to existing multiview image datasets. Compared to prior multiview generative models that require per-scene optimization for 3D reconstruction, Bolt3D reduces the inference cost by a factor of up to 300 times.

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Code Implementations(1)

Apache-2.0

🌍 WorldGen - Generate Any 3D Scene in Seconds

2,144199PythonApr 17, 20256 months agoApache-2.0
3d-generation3d-reconstructiongenerative-aigraphicsimage-to-3d+4 more

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Cite this paper

@article{szymanowicz2025boltd,
  title  = {Bolt3D: Generating 3D Scenes in Seconds},
  author = {Stanislaw Szymanowicz and Jason Y. Zhang and Pratul Srinivasan and Ruiqi Gao and Arthur Brussee and Aleksander Holynski and Ricardo Martin-Brualla and Jonathan T. Barron and Philipp Henzler},
  year   = {2025},
  eprint = {2503.14445},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.14445v2}
}

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