01847nas a2200181   4500000000100000000000100001008004100002100000900043700001100052700001000063700001000073700001100083700001200094245012500106490000700231520141300238022001401651                                       d1 aH Li1 aJ Fang1 aY Jia1 aLQ Ji1 aX Chen1 aNY Wang00aThangka Sketch Colorization Based on Multi-Level Adaptive-Instance-Normalized Color Fusion and Skip Connection Attention0 v123 aThangka is an important intangible cultural heritage of Tibet. Due to the complexity, and time-consuming nature of the Thangka painting technique, this technique is currently facing the risk of being lost. It is important to preserve the art of Thangka through digital painting methods. Machine learning-based auto-sketch colorization is one of the vital steps for digital Thangka painting. However, existing learning-based sketch colorization methods face two challenges in solving the problem of colorizing Thangka: (1) the extremely rich colors of the Thangka make it difficult to color accurately with existing algorithms, and (2) the line density of the Thangka brings extreme challenges for algorithms to define what semantic information the lines imply. To resolve these problems, we propose a Thangka sketch colorization method based on multi-level adaptive-instance-normalized color fusion (MACF) and skip connection attention (SCA). The proposed method consists of two parts: (1) a multi-level adaptive-instance-normalized color fusion (MACF) to fuse sketch feature and color feature; and (2) a skip connection attention (SCA) mechanism to distinguish the semantic information implied by the sketch lines. Experiments on colorizing Thangka sketches show that our method works well on two small datasets-the Danbooru 2019 dataset and the Thangka dataset. Our approach can generate exquisite Thangka.  a2079-9292