  Thanks to advances in deep learning techniques, Human Pose Estimation (HPE)
has achieved significant progress in natural scenarios. However, these models
perform poorly in artificial scenarios such as painting and sculpture due to
the domain gap, constraining the development of virtual reality and augmented
reality. With the growth of model size, retraining the whole model on both
natural and artificial data is computationally expensive and inefficient. Our
research aims to bridge the domain gap between natural and artificial scenarios
with efficient tuning strategies. Leveraging the potential of language models,
we enhance the adaptability of traditional pose estimation models across
diverse scenarios with a novel framework called VLPose. VLPose leverages the
synergy between language and vision to extend the generalization and robustness
of pose estimation models beyond the traditional domains. Our approach has
demonstrated improvements of 2.26% and 3.74% on HumanArt and MSCOCO,
respectively, compared to state-of-the-art tuning strategies.
