  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.
