How does GCN handle different set of input feature lines

[longbowzhang]

Hi @kv2000 , Superb work and dataset, which in my opinion is a big contribution to this research area. Meanwhile, I have several questions. [1] some inconsistency As mentioned in the abstract, there are 563 garment instances. But according to table 1, the total number is 599. I suppose you remove some garment instances. But why, e.g., poor SMPL fitting results?

How many random views are used for rendering synthetic images, 5 (section 4.1) or 3 (section 4.3)? I admit that this does not really matter but somewhat confusing.

[2] Why ignore the shape (i.e., beta) parameter of the SMPL model from the whole pipeline? The scale of a kid’s cloth is different from that of an adult’s cloth.

[3] The gcn part. In the original Pixel2Mesh paper, the input is always the fixed ellipsoid from which the deformations are. But in your paper, the input is the varying set of feature lines depending on the category of the cloth (as shown in Fig. 3), right? How does the GCN manage to handle varying set of feature lines?

Thanks very much!