VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning

arXiv:2402.14456v1 Announce Type: new
Abstract: 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 …

abstract advances artificial arxiv cs.cv deep learning deep learning techniques development domain gap hpe human language natural painting progress reality type virtual virtual reality vision