Open AccessJournal Article
Example-based Automatic Portraiture
TL;DR: In this paper, a new approach for automatically generating a life-like portrait from a frontal face image is presented. But unlike previous texture synthesis and image synthesis works that assumed modeling is homogeneous, Inhomogeneous Markov Random Field Model is employed as the statistical model, and a nonparametric sampling scheme is used to capture the complex statistical characteristics of face image and corresponding artist drawing in this paper.
read more
Abstract: In this paper, we present a new approach for automatically generating a life-like portrait from a frontal face image. We learn the portraiture from a set of real artwork examples. Different from previous texture synthesis and image synthesis works that assumed modeling is homogeneous, Inhomogeneous Markov Random Field Model is employed as the statistical model, and a non-parametric sampling scheme is used to capture the complex statistical characteristics of face image and corresponding artist drawing in this paper. In our strategy, only those pixels corresponding to a portrait point are sampled. Such a strategy is crucial for maintaining facial structure and guaranteeing coherence of portrait lines. Experimental results demonstrate the effectiveness and life-likeness of our approach.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Example-based composite sketching of human portraits
Hong Chen,Ziqiang Liu,Chuck Rose,Ying-Qing Xu,Heung-Yeung Shum,David Salesin +5 more
- 07 Jun 2004
TL;DR: This work proposes a system which combines two separate but similar subsystems, one for the face and another for the hair, each of which employs a global and a local model.
Portrait painting using active templates
Mingtian Zhao,Song-Chun Zhu +1 more
- 05 Aug 2011
TL;DR: This paper presents an example-based method to render portrait paintings from photographs, by transferring brush strokes from previously painted portrait templates by artists, with a dictionary of portrait painting templates for different types of faces.
68
Facial metamorphosis using geometrical methods for biometric applications
TL;DR: A novel approach to the problem of expression modeling and morphing based on a geometry-based paradigm that combines the traditional free-form deformation model with data interpolation techniques based on the proximity preserving Voronoi diagram is suggested.
47
Guided Face Cartoon Synthesis
TL;DR: A new method, called guided synthesis, to synthesize a face cartoon from a face photo, which generates a cartoon image by incorporating the content of guidance images taken from the training set.
34
FCN based preprocessing for exemplar-based face sketch synthesis
TL;DR: Extensive experiments on public face sketch datasets verify that the proposed two-stage method improves the sketch synthesis quality of the state-of-the-art exemplar-based methods in terms of both recognition accuracy and perceptual quality.
16
References
Texture synthesis by non-parametric sampling
Alexei A. Efros,Thomas Leung +1 more
- 20 Sep 1999
TL;DR: A non-parametric method for texture synthesis that aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
Image analogies
Aaron Hertzmann,Charles E. Jacobs,Nuria Oliver,Brian Curless,David Salesin +4 more
- 01 Aug 2001
TL;DR: This paper describes a new framework for processing images by example, called “image analogies,” based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis.
Fast texture synthesis using tree-structured vector quantization
Li-Yi Wei,Marc Levoy +1 more
- 01 Jul 2000
TL;DR: This paper presents an efficient algorithm for realistic texture synthesis derived from Markov Random Field texture models and generates textures through a deterministic searching process that accelerates this synthesis process using tree-structured vector quantization.
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
TL;DR: The resulting model, called FRAME (Filters, Random fields And Maximum Entropy), is a Markov random field (MRF) model, but with a much enriched vocabulary and hence much stronger descriptive ability than the previous MRF models used for texture modeling.
Related Papers (5)
[...]
Aaron Hertzmann,Charles E. Jacobs,Nuria Oliver,Brian Curless,David Salesin +4 more
- 01 Aug 2001