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DreamBooth3D: Subject-Driven Text-to-3D Generation

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arxiv 2303.13508 v2 pith:7MH6BZ2U submitted 2023-03-23 cs.CV cs.AIcs.GR

DreamBooth3D: Subject-Driven Text-to-3D Generation

Amit Raj , Srinivas Kaza , Ben Poole , Michael Niemeyer , Nataniel Ruiz , Ben Mildenhall , Shiran Zada , Kfir Aberman
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Michael Rubinstein Jonathan Barron Yuanzhen Li Varun Jampani
This is my paper
classification cs.CV cs.AIcs.GR
keywords modelssubjecttext-to-3dtext-to-imageapproachassetsdreambooth3dgeneration
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present DreamBooth3D, an approach to personalize text-to-3D generative models from as few as 3-6 casually captured images of a subject. Our approach combines recent advances in personalizing text-to-image models (DreamBooth) with text-to-3D generation (DreamFusion). We find that naively combining these methods fails to yield satisfactory subject-specific 3D assets due to personalized text-to-image models overfitting to the input viewpoints of the subject. We overcome this through a 3-stage optimization strategy where we jointly leverage the 3D consistency of neural radiance fields together with the personalization capability of text-to-image models. Our method can produce high-quality, subject-specific 3D assets with text-driven modifications such as novel poses, colors and attributes that are not seen in any of the input images of the subject.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation

    cs.CV 2023-09 unverdicted novelty 7.0

    DreamGaussian creates high-quality textured 3D meshes from single-view images in 2 minutes via generative Gaussian Splatting with mesh extraction and UV refinement.

  2. SyncDreamer: Generating Multiview-consistent Images from a Single-view Image

    cs.CV 2023-09 unverdicted novelty 6.0

    SyncDreamer produces multiview-consistent images from a single input image by jointly modeling their distribution and synchronizing intermediate diffusion states via 3D-aware attention.

  3. MVDream: Multi-view Diffusion for 3D Generation

    cs.CV 2023-08 conditional novelty 6.0

    MVDream is a multi-view diffusion model that functions as a generalizable 3D prior, enabling more consistent text-to-3D generation and few-shot 3D concept learning from 2D examples.

  4. Few-step Flow for 3D Generation via Marginal-Data Transport Distillation

    cs.CV 2025-09 conditional novelty 5.0

    MDT-dist distills a pretrained 3D flow model into a 1-2 step generator using velocity matching plus velocity distillation, cutting TRELLIS inference from 6.1s to 0.68s while approximately preserving generation quality.