Flow-Guided Attention Deformation for Person Image Generation.

Pose-guided person image generation aims to transfer reference images to target poses while preserving the source appearance. Recent approaches achieve considerable improvement by using spatial transformation modules such as attention operation. However, the commonly used vanilla attention tends to generate a dense correlation matrix which means that the value of a target position is the weighted sum of many source positions, resulting in blurry appearance. In this paper, we propose a novel model named Flow-guided Attention Deformation (FAD) to perform the spatial transformation. Our model first establishes the correlation between sources and targets with a flow-guided attention operation. Then, with the obtained correlation matrix, we perform an accurate deformation for source features to generate the predicted image. Extensive results demonstrate the superiority of the proposed method, outperforming state-of-the-art methods quantitatively and qualitatively. Ablation studies clarify the efficiency of the proposed modules and verify our hypothesis.