- 专利标题: TRAINING MACHINE LEARNING MODELS TO PERFORM NEURAL STYLE TRANSFER IN THREE-DIMENSIONAL SHAPES
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申请号: US18149605申请日: 2023-01-03
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公开(公告)号: US20230326158A1公开(公告)日: 2023-10-12
- 发明人: Hooman SHAYANI , Marco FUMERO , Aditya SANGHI
- 申请人: AUTODESK, INC.
- 申请人地址: US CA San Francisco
- 专利权人: AUTODESK, INC.
- 当前专利权人: AUTODESK, INC.
- 当前专利权人地址: US CA San Francisco
- 主分类号: G06T19/20
- IPC分类号: G06T19/20 ; G06N3/0475 ; G06N3/092
摘要:
One embodiment of the present invention sets forth a technique for training a machine learning model to perform style transfer. The technique includes applying one or more augmentations to a first input three-dimensional (3D) shape to generate a second input 3D shape. The technique also includes generating, via a first set of neural network layers, a style code based on a first latent representation of the first input 3D shape and a second latent representation of the second input 3D shape. The technique further includes generating, via a second set of neural network layers, a first output 3D shape based on the style code and the second latent representation, and performing one or more operations on the first and second sets of neural network layers based on a first loss associated with the first output 3D shape to generate a trained machine learning model.
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