Portrait Stylization: Artistic Style Transfer with Auxiliary Networks for Human Face Stylization
Not on the map. Why?
Abstract (arXiv)
Today's image style transfer methods have difficulty retaining humans face individual features after the whole stylizing process. This occurs because the features like face geometry and people's expressions are not captured by the general-purpose image classifiers like the VGG-19 pre-trained models. This paper proposes the use of embeddings from an auxiliary pre-trained face recognition model to encourage the algorithm to propagate human face features from the content image to the final stylized result.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 23 Sep 2023.
Builds on
9 of 12 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 914
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2015A Neural Algorithm of Artistic Stylecited by 483
- 2014Understanding Deep Image Representations by Inverting Themcited by 403
- 2016Controlling Perceptual Factors in Neural Style Transfercited by 100
- 2019Real-time Facial Surface Geometry from Monocular Video on Mobile GPUscited by 73
Built on by
Not cited yet.