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VolumeDiffusion: Flexible Text-to-3D Generation with Efficient Volumetric Encoder

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arxiv 2312.11459 v3 pith:SHYA434D submitted 2023-12-18 cs.CV

Zhicong Tang , Shuyang Gu , Chunyu Wang , Ting Zhang , Jianmin Bao , Dong Chen , Baining Guo This is my paper · ORCID
classification cs.CV
keywords generationmodelobjecttext-to-3dvolumesdiffusionefficientencoder
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This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology.

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Cited by 5 Pith papers

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  3. Gaussian Variation Field Diffusion for High-fidelity Video-to-4D Synthesis

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    A video-to-4D model that encodes mesh animations into compact Gaussian variation latents and diffuses them conditioned on the video and a canonical Gaussian splat.

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

    cs.CV 2025-06 conditional novelty 6.0 of 10

    From a single image, a 3D latent diffusion model generates all parts of an object at once by packing the part structure into two non-overlapping volumes.

  5. Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation

    cs.GR 2025-08 unverdicted novelty 4.0 of 10

    A visual prompt engineering system for text-to-3D generation uses multi-view MLLM scoring and interactive visualizations to help designers create models faster, with 70.5% time reduction and higher quality ratings (4....

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