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While models of 3D clothing learned from real data exist, no method canpredict clothing deformation as a function of garment size. In this paper, weintroduce SizerNet to predict 3D clothing conditioned on human body shape andgarment size parameters, and ParserNet to infer garment meshes and shape underclothing with personal details in a single pass from an input mesh. SizerNetallows to estimate and visualize the dressing effect of a garment in varioussizes, and ParserNet allows to edit clothing of an input mesh directly,removing the need for scan segmentation, which is a challenging problem initself. To learn these models, we introduce the SIZER dataset of clothing sizevariation which includes $100$ different subjects wearing casual clothing itemsin various sizes, totaling to approximately 2000 scans. This dataset includesthe scans, registrations to the SMPL model, scans segmented in clothing parts,garment category and size labels. Our experiments show better parsing accuracyand size prediction than baseline methods trained on SIZER. The code, model anddataset will be released for research purposes.

SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing