  Recently customized generation has significant potential, which uses as few
as 3-5 user-provided images to train a model to synthesize new images of a
specified subject. Though subsequent applications enhance the flexibility and
diversity of customized generation, fine-grained control over the given subject
acting like the person's pose is still lack of study. In this paper, we propose
a PersonificationNet, which can control the specified subject such as a cartoon
character or plush toy to act the same pose as a given referenced person's
image. It contains a customized branch, a pose condition branch and a structure
alignment module. Specifically, first, the customized branch mimics specified
subject appearance. Second, the pose condition branch transfers the body
structure information from the human to variant instances. Last, the structure
alignment module bridges the structure gap between human and specified subject
in the inference stage. Experimental results show our proposed
PersonificationNet outperforms the state-of-the-art methods.
