Abstract: Traditional head avatar reconstruction methods are mostly based on 3D Morphable Models(3DMM),which,while convenient for animating,cannot represent non-rigid structures like hairs.Recently,head avatar approaches based on the neural radiance field achieve impressive visual results but suffer from shortcomings in animation and training efficiency.To address these issues,monocular videos are used as raw data,and a dynamically expanding point cloud is utilized,to construct an animatable virtual head avatar.The point cloud can be rapidly rendered into images by rasterization,significantly reducing training time.In terms of texture representation,color is decoupled into albedo and shading,with shading further decomposed into normal and a combination of region features obtained through sparse encoding of points,resulting in more precise textures.However,the inherent discreteness of point clouds can lead to holes.Therefore,a normal smoothing strategy is employed to enhance texture continuity,successfully eliminating texture holes in regions like teeth and tongue.A large number of experiments on multiple subjects show that compared to the state-of-the-art head avatar construction algorithms,such as IMavatar,PointAvatar,NerFace,and StyleAvatar,the animatable avatars constructed based on point clouds,combined with region encoding and normal smoothing strategy,exhibit an improvement of average 3.41% on the PSNR metric.Ablation experiments show that the PSNR metric is improved by approximately 3.50% and 3.44% respectively over not using region encoding and normal smoothing strategy.

Key words: Head avatar, 3D reconstruction, Region encoding, Point cloud, Rasterization, Deep learning