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The paper introduces 3DreamBooth, a framework for generating view-consistent videos of customized subjects by decoupling spatial geometry from temporal motion through a 1-frame optimization paradigm. To address the lack of comprehensive spatial priors in existing 2D-centric approaches, 3DreamBooth restricts updates to spatial representations, effectively baking a robust 3D prior into the model without requiring extensive video training. The framework also incorporates 3Dapter, a visual conditioning module, to enhance fine-grained textures and accelerate convergence via multi-view joint optimization.
Forget generating plausible-but-fake details: 3DreamBooth bakes a robust 3D prior into video generation models using only a single-frame optimization, enabling truly view-consistent customized subject videos.
Creating dynamic, view-consistent videos of customized subjects is highly sought after for a wide range of emerging applications, including immersive VR/AR, virtual production, and next-generation e-commerce. However, despite rapid progress in subject-driven video generation, existing methods predominantly treat subjects as 2D entities, focusing on transferring identity through single-view visual features or textual prompts. Because real-world subjects are inherently 3D, applying these 2D-centric approaches to 3D object customization reveals a fundamental limitation: they lack the comprehensive spatial priors necessary to reconstruct the 3D geometry. Consequently, when synthesizing novel views, they must rely on generating plausible but arbitrary details for unseen regions, rather than preserving the true 3D identity. Achieving genuine 3D-aware customization remains challenging due to the scarcity of multi-view video datasets. While one might attempt to fine-tune models on limited video sequences, this often leads to temporal overfitting. To resolve these issues, we introduce a novel framework for 3D-aware video customization, comprising 3DreamBooth and 3Dapter. 3DreamBooth decouples spatial geometry from temporal motion through a 1-frame optimization paradigm. By restricting updates to spatial representations, it effectively bakes a robust 3D prior into the model without the need for exhaustive video-based training. To enhance fine-grained textures and accelerate convergence, we incorporate 3Dapter, a visual conditioning module. Following single-view pre-training, 3Dapter undergoes multi-view joint optimization with the main generation branch via an asymmetrical conditioning strategy. This design allows the module to act as a dynamic selective router, querying view-specific geometric hints from a minimal reference set. Project page: https://ko-lani.github.io/3DreamBooth/
