Enhancing Human Pose Transfer with Convolutional Block Attention Module and Facial Loss Optimization

Pose transfer methods often struggle to simultaneously preserve fine-grained clothing textures and facial details, especially under large pose variations. To address these limitations, we propose a model based on the Multi-scale attention guided pose transfer model, with modifications to its residual block by integrating the convolutional block attention module and changing the activation function from ReLU to Mish to capture more features related to clothing and skin color. Additionally, as the generated images had facial features differing from the original image, we propose two different facial feature loss functions to help the model learn more precise image features. According to the experimental results, the proposed method demonstrates superior performance compared to the Multi-scale Attention Guided Pose Transfer (MAGPT) on the DeepFashion dataset, achieving a 3.41% reduction in FID, a 0.65% improvement in SSIM, a 2% decrease in LPIPS, and a 2.7% decrease in LPIPS. Ultimately, only one reference image is required to enable users to transform into different action videos with the proposed system architecture.