Zhang et al., 2022 - Google Patents
3d-aware semantic-guided generative model for human synthesisZhang et al., 2022
View PDF- Document ID
- 6032578839810883949
- Author
- Zhang J
- Sangineto E
- Tang H
- Siarohin A
- Zhong Z
- Sebe N
- Wang W
- Publication year
- Publication venue
- European Conference on Computer Vision
External Links
Snippet
Abstract Generative Neural Radiance Field (GNeRF) models, which extract implicit 3D representations from 2D images, have recently been shown to produce realistic images representing rigid/semi-rigid objects, such as human faces or cars. However, they usually …
- 230000015572 biosynthetic process 0 title abstract description 11
Classifications
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- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
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- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
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- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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