Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402
Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402 Title: Unsupervised Training for 3D Morphable Model Regression Abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University
Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402 Title: Unsupervised Training for 3D Morphable Model Regression Abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University
Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402 Title: Unsupervised Training for 3D Morphable Model Regression Abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University _______________________________________________ talks mailing list talks@lists.cs.princeton.edu To edit subscription settings or remove yourself, use this link: https://lists.cs.princeton.edu/mailman/listinfo/talks
participants (1)
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Barbara A. Mooring