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We present DreamBooth3D, an approach to personalize text-to-3D generativemodels from as few as 3-6 casually captured images of a subject. Our approachcombines recent advances in personalizing text-to-image models (DreamBooth)with text-to-3D generation (DreamFusion). We find that naively combining thesemethods fails to yield satisfactory subject-specific 3D assets due topersonalized text-to-image models overfitting to the input viewpoints of thesubject. We overcome this through a 3-stage optimization strategy where wejointly leverage the 3D consistency of neural radiance fields together with thepersonalization capability of text-to-image models. Our method can producehigh-quality, subject-specific 3D assets with text-driven modifications such asnovel poses, colors and attributes that are not seen in any of the input imagesof the subject.

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