High-definition ancient paintings have complex structures composed of interlaced drawing curves and distinct canvas textures. Aiming at restoring high-definition ancient Chinese paintings naturally, this paper presented an interactive repairing method based on decomposition of drawing curves. First, a painting was parsed into contents and canvases. Next, our method decomposed the contents into individual curves and repaired the curves one by one, based on Tensor Voting and limited user assistance. In the meanwhile, we adopted an exemplar-based method to restore canvases. At last, we merged the repaired contents and canvases. Taking multiple high-definition images of ancient Chinese paintings as experimental data, we val- idated the proposed method and compared its results with those obtained by state-of-the-art methods, e.g. Laplacian inpainting, subjectively and quantitatively. Experimental results show that the proposed method performs better in natural completion of high-definition ancient Chinese paintings.