When reading the paper, I've got the impression that the paper is not finished with couple of key experiments missing. Some parts of the paper lack motivation. Terminology is sometimes unclear and ambiguous. 1. Terminology. The paper uses terms "animation", "generative", "interpolation". See contribution 1 in L40-42. While the paper reported some interpolation experiments, I haven't found any animation or generation experiments. It seems the authors equate interpolation and animation (Section 4.2) which is not correct. I consider animation is a physically plausible motion. Like a person opens a mouth, car moves, while interpolation is just warping one image into the other. Fig 7 shows exactly this with the end states being plausible states of the system. The authors should fix the ambiguity to avoid misunderstanding. The authors also don't report any generation results. Can I sample a random shape from the learnt distribution? If not the I don't think it's correct to say the model is generative. 2. Motivation. It's not clear why the problem is important from practical standpoint? Why one would like to interpolate between two icons? Motivation behind animation is more clear, but in my opinion, the paper doesn't do animation. I believe from a practical standpoint letting the user to input text and be able to generate an icon would also be important. Again, I have hard time understanding why shape autoencoding and interpolation is interesting. 3. Experiments. Probably the biggest concern with the paper is with the experiments. The paper reports only self comparisons. The paper also doesn't explain why this is so, which adds to the poor motivation problem. In a generative setting comparisons with SketchRNN could be performed.
3. Experiments. Probably the biggest concern with the paper is with the experiments. The paper reports only self comparisons. The paper also doesn't explain why this is so, which adds to the poor motivation problem. In a generative setting comparisons with SketchRNN could be performed.