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.

加入定制化物体到图像生成中，我们引入了CustomNet，一个新的物体定制方法，通过显式地引入3D新视角合成能力来调整空间位置关系和视角，以有效地保留物体的身份信息，并通过文本描述或用户定义的图像实现位置和背景的灵活控制，从而实现了对视角、位置和背景的零测试时间优化的控制，并且增强了身份保护性和生成了多样而和谐的输出。

CustomNet: 零样本目标定制与可变视角在文本至图像扩散模型中的应用