Recently, high-quality, humanoid-like 3D point clouds have become extensively used in various use cases related to VR/AR applications. Such high-density point clouds, represented by a huge number of points (e.g., 1 million points) carrying various photometric attributes, require efficient compression techniques for storage and transmission. However, most research works in the literature mainly focus on geometry compression, while only a few consider the spatio-temporal compression of color attributes. In this paper, we propose a novel color attribute prediction method, which exploits a skeleton-based affine motion estimation technique. The skeleton and the motion parameters are compressed in a lossless manner, to preserve accurate color prediction. The color residuals are lossy compressed using a video-based coding solution. Our proposal has been integrated into the Video-based Point Cloud Compression (V-PCC) test model of MPEG. The experimental results demonstrate that the proposed method outperforms the reference V-PCC test model, notably in low bitrate conditions.