We introduce a deep learning based framework for modeling dynamic hairs from monocular videos, which could be captured by a commodity video camera or downloaded from Internet. The framework mainly consists of two neural networks, i.e., HairSpatNet for inferring 3D spatial features of hair geometry from 2D image features, and HairTempNet for extracting temporal features of hair motions from video frames. The spatial features are represented as 3D occupancy fields depicting the hair volume shapes and 3D orientation fields indicating the hair growing directions. The temporal features are represented as bidirectional 3D warping fields, describing the forward and backward motions of hair strands cross adjacent frames. Both HairSpatNet and HairTempNet are trained with synthetic hair data. The spatial and temporal features predicted by the networks are subsequently used for growing hair strands with both spatial and temporal consistency. Experiments demonstrate that our method is capable of constructing plausible dynamic hair models that closely resemble the input video, and compares favorably to previous single-view techniques.
In this paper, we present iOrthoPredictor, a novel system to visually predict teeth alignment in photographs. 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.
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