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3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors

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arxiv 2403.02234 v2 pith:NDV32FUK submitted 2024-03-04 cs.CV

Fangzhou Hong , Jiaxiang Tang , Ziang Cao , Min Shi , Tong Wu , Zhaoxi Chen , Shuai Yang , Tengfei Wang
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Liang Pan Dahua Lin Ziwei Liu
This is my paper · ORCID
classification cs.CV
keywords diffusiondtopiamodelsgenerationpriorsstagesystemcoarse
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a two-stage text-to-3D generation system, namely 3DTopia, which generates high-quality general 3D assets within 5 minutes using hybrid diffusion priors. The first stage samples from a 3D diffusion prior directly learned from 3D data. Specifically, it is powered by a text-conditioned tri-plane latent diffusion model, which quickly generates coarse 3D samples for fast prototyping. The second stage utilizes 2D diffusion priors to further refine the texture of coarse 3D models from the first stage. The refinement consists of both latent and pixel space optimization for high-quality texture generation. To facilitate the training of the proposed system, we clean and caption the largest open-source 3D dataset, Objaverse, by combining the power of vision language models and large language models. Experiment results are reported qualitatively and quantitatively to show the performance of the proposed system. Our codes and models are available at https://github.com/3DTopia/3DTopia

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Forward citations

Cited by 5 Pith papers

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    PixGS is a single-stage pixel-space diffusion model that directly produces high-quality 3D Gaussian Splats from text or images in ~1s, outperforming multi-stage latent methods on standard benchmarks.

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  4. Efficient Part-level 3D Object Generation via Dual Volume Packing

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  5. Advancing high-fidelity 3D and Texture Generation with 2.5D latents

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    A 2.5D latent combining multiview RGB, normal, and coordinate images, generated by a mixture-of-LoRA fine-tuned Flux model, enables joint 3D geometry and texture generation from text or images.

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