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Our system takes a frontal face image of a patient with visible malpositioned teeth along with a corresponding 3D teeth model as input, and generates a facial image with aligned teeth, simulating a real orthodontic treatment effect. The key enabler of our method is an effective disentanglement of an explicit representation of the teeth geometry from the in-mouth appearance, where the accuracy of teeth geometry transformation is ensured by the 3D teeth model while the in-mouth appearance is modeled as a latent variable. The disentanglement enables us to achieve fine-scale geometry control over the alignment while retaining the original teeth appearance attributes and lighting conditions. The whole pipeline consists of three deep neural networks: a U-Net architecture to explicitly extract the 2D teeth silhouette maps representing the teeth geometry in the input photo, a novel multilayer perceptron (MLP) based network to predict the aligned 3D teeth model, and an encoder-decoder based generative model to synthesize the in-mouth appearance conditional on the original teeth appearance and the aligned teeth geometry. Extensive experimental results and a user study demonstrate that iOrthoPredictor is effective in qualitatively predicting teeth alignment, and applicable to the orthodontic industry.<\/jats:p>","DOI":"10.1145\/3414685.3417771","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T21:51:05Z","timestamp":1606513865000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["iOrthoPredictor"],"prefix":"10.1145","volume":"39","author":[{"given":"Lingchen","family":"Yang","sequence":"first","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zefeng","family":"Shi","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiqian","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Fu","sequence":"additional","affiliation":[{"name":"City University of Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"International Conference on Machine Learning (ICML). 40--49","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas , Olga Diamanti , Ioannis Mitliagkas , and Leonidas Guibas . 2018 . 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