  Human motion transfer (HMT) aims to generate a video clip for the target
subject by imitating the source subject's motion. Although previous methods
have achieved good results in synthesizing good-quality videos, they lose sight
of individualized motion information from the source and target motions, which
is significant for the realism of the motion in the generated video. To address
this problem, we propose a novel identity-preserved HMT network, termed
\textit{IDPres}. This network is a skeleton-based approach that uniquely
incorporates the target's individualized motion and skeleton information to
augment identity representations. This integration significantly enhances the
realism of movements in the generated videos. Our method focuses on the
fine-grained disentanglement and synthesis of motion. To improve the
representation learning capability in latent space and facilitate the training
of \textit{IDPres}, we introduce three training schemes. These schemes enable
\textit{IDPres} to concurrently disentangle different representations and
accurately control them, ensuring the synthesis of ideal motions. To evaluate
the proportion of individualized motion information in the generated video, we
are the first to introduce a new quantitative metric called Identity Score
(\textit{ID-Score}), motivated by the success of gait recognition methods in
capturing identity information. Moreover, we collect an identity-motion paired
dataset, $Dancer101$, consisting of solo-dance videos of 101 subjects from the
public domain, providing a benchmark to prompt the development of HMT methods.
Extensive experiments demonstrate that the proposed \textit{IDPres} method
surpasses existing state-of-the-art techniques in terms of reconstruction
accuracy, realistic motion, and identity preservation.
