Garment Modeling with a Depth Camera

Xiaowu Chen    Bin Zhou    Feixiang Lu    Lin Wang    Lang Bi                                            Ping Tan

State Key Laboratory of Virtual Reality Technology & Systems, Beihang University        Simon Fraser University


Given an RGBD sequence of a dressed garment, our system produces a detailed high quality 3D garment model by detecting and assembling suitable components. Each color in the models denotes a different component.


Previous garment modeling techniques mainly focus on designing novel garments to dress up virtual characters. We study the modeling of real garments and develop a system that is intuitive to use even for novice users. Our system includes garment component detectors and design attribute classifiers learned from a manually labeled garment image database. In the modeling time, we scan the garment with a Kinect and build a rough shape by KinectFusion from the raw RGBD sequence. The detectors and classifiers will identify garment components (e.g. collar, sleeve, pockets, belt, and buttons) and their design attributes (e.g. falbala collar or lapel collar, hubble-bubble sleeve or straight sleeve) from the RGB images. Our system also contains a 3D deformable template database for garment components. Once the components and their designs are determined, we choose appropriate templates, stitch them together, and fit them to the initial garment mesh generated by KinectFusion. Experiments on various different garment styles consistently generate high quality results.


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    author = {Xiaowu Chen and Bin Zhou and Feixiang Lu and Lin Wang and Lang Bi and Ping Tan},

    title = {Garment Modeling with a Depth Camera},

    journal = {ACM Transactions on Graphics (Proc. SIGGRAPH ASIA)},

    volume = {34},

    number = {6},

    pages = {203:1--203:12},

    year = {2015}


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