CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models
This addresses the need for efficient and high-quality object customization in text-to-image models, though it appears incremental by building on existing methods.
The paper tackles the problem of customizing objects in text-to-image generation by introducing CustomNet, which integrates 3D novel view synthesis to adjust viewpoints and spatial relationships, resulting in enhanced identity preservation and diverse outputs without test-time optimization.
Incorporating a customized object into image generation presents an attractive feature in text-to-image generation. However, existing optimization-based and encoder-based methods are hindered by drawbacks such as time-consuming optimization, insufficient identity preservation, and a prevalent copy-pasting effect. To overcome these limitations, we introduce CustomNet, a novel object customization approach that explicitly incorporates 3D novel view synthesis capabilities into the object customization process. This integration facilitates the adjustment of spatial position relationships and viewpoints, yielding diverse outputs while effectively preserving object identity. Moreover, we introduce delicate designs to enable location control and flexible background control through textual descriptions or specific user-defined images, overcoming the limitations of existing 3D novel view synthesis methods. We further leverage a dataset construction pipeline that can better handle real-world objects and complex backgrounds. Equipped with these designs, our method facilitates zero-shot object customization without test-time optimization, offering simultaneous control over the viewpoints, location, and background. As a result, our CustomNet ensures enhanced identity preservation and generates diverse, harmonious outputs.