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• W2988438046 abstract "Editing 3D models is a very challenging task, as it requires complex interactions with the 3D shape to reach the targeted design, while preserving the global consistency and plausibility of the shape. In this work, we present an intelligent and user-friendly 3D editing tool, where the edited model is constrained to lie onto a learned manifold of realistic shapes. Due to the topological variability of real 3D models, they often lie close to a disconnected manifold, which cannot be learned with a common learning algorithm. Therefore, our tool is based on a new deep learning model, DiscoNet, which extends 3D surface autoencoders in two ways. Firstly, our deep learning model uses several autoencoders to automatically learn each connected component of a disconnected manifold, without any supervision. Secondly, each autoencoder infers the output 3D surface by deforming a pre-learned 3D template specific to each connected component. Both advances translate into improved 3D synthesis, thus enhancing the quality of our 3D editing tool." @default.
• W2988438046 created "2019-11-22" @default.
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• W2988438046 date "2019-10-01" @default.
• W2988438046 modified "2023-09-25" @default.
• W2988438046 title "DiscoNet: Shapes Learning on Disconnected Manifolds for 3D Editing" @default.
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• W2988438046 doi "https://doi.org/10.1109/iccv.2019.00357" @default.
• W2988438046 hasPublicationYear "2019" @default.
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