  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.
