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.

翻译：我们提出了DreamBooth3D，一种仅需3-6张随意拍摄的主题图像即可个性化文本到三维生成模型的方法。该方法结合了文本到图像模型个性化（DreamBooth）与文本到三维生成（DreamFusion）的最新进展。我们发现，直接结合这两种方法无法获得令人满意的主题特定三维资产，原因在于个性化文本到图像模型对主题输入视角的过拟合。为此，我们提出了一种三阶段优化策略，同时利用神经辐射场的三维一致性与文本到图像模型的个性化能力。该方法可生成高质量的主题特定三维资产，并支持文本驱动的修改，例如主题输入图像中未出现的新姿势、颜色和属性。