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Comparison of different surface representations in blending process by applying random scaling factors to each representation: (a) the original face model, (b) vertex displacement representation, and (c) affine transformation representation.
Source publication
This paper describes a practical technique for 3D artistic face modeling
where a human identity can be inserted into a 3D artistic face.
This approach can automatically extract the human identity from
a 3D human face model and then transfer it to a 3D artistic face
model in a controllable manner. Its core idea is to construct a face
geometry space...
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Citations
... e detection of objects in real-time sonar images is utilized for the network based training [26]. e 3D artistic face modeling with controllable manner [27] enables the face geometry space and a face texture space based on 3D face dataset. e experiment is carried out in real time without GPU acceleration to achieve different cartoon characters. ...
Humans have mastered the skill of creativity for many decades. The process of replicating this mechanism is introduced recently by using neural networks which replicate the functioning of human brain, where each unit in the neural network represents a neuron, which transmits the messages from one neuron to other, to perform subconscious tasks. Usually, there are methods to render an input image in the style of famous art works. This issue of generating art is normally called nonphotorealistic rendering. Previous approaches rely on directly manipulating the pixel representation of the image. While using deep neural networks which are constructed using image recognition, this paper carries out implementations in feature space representing the higher levels of the content image. Previously, deep neural networks are used for object recognition and style recognition to categorize the artworks consistent with the creation time. This paper uses Visual Geometry Group (VGG16) neural network to replicate this dormant task performed by humans. Here, the images are input where one is the content image which contains the features you want to retain in the output image and the style reference image which contains patterns or images of famous paintings and the input image which needs to be style and blend them together to produce a new image where the input image is transformed to look like the content image but “sketched” to look like the style image.
... Entre las propuestas de diseño de Gonzalo Guerrero, se eligió una con características estilizadas, aplicando trazos fuertes y manchas en función del appeal del personaje. El término cartoonish difiere de la caricatura, pues si bien ambos términos son una representación que reflejan la identidad humana, la diferencia radica en que, mientras una caricatura intenta exagerar una identidad humana, un cartoon intenta mantener un estilo artístico (Sucontphunt, 2014). ...
The concept art is the bridge between the narrative and the stages of production (models, structures, animation, effects, textures). In order to achieve an effective transition, it´s necessary, from the concept art as an intermediary between plastic and mechanics, to construct the general aesthetic of the animated project from the character design and objects with the acting to generate an empathic bond with the public. The systematization of the experience will be focused on the main character, Gonzalo Guerrero Aroza, of the animated project, “Chronicles of the Edge of the World”, who will be the basis for the application of the plastic-mechanical dynamics that originate from the study of the visual and movement functions of the Character to achieve the appeal, and it works as a model for the creation of other characters or objects of animation.
... Mingming et al. [17] exaggerated face features at both global and local levels, where the input face is first classified and deformed with pre-defined templates and then refined by exaggerating facial features that are different from the mean. Most recently, Sucontphunt [20,21] modeled 3D artistic face by blending a real face with an artistic model in a PCA space spanned by both real face and artistic model. ...
We present a data-driven method for automatically generating a 3D cartoon of a real 3D face. Given a sparse set of 3D real faces and their corresponding cartoon faces modeled by an artist, our method models the face in each subspace as the deformation of its nearby exemplars and learn a mapping between the deformations defined by the real faces and their cartoon counterparts. To reduce the exemplars needed for learning, we regress a collection of linear mappings defined locally in both face geometry and identity spaces and develop a progressive scheme for users to gradually add new exemplars for training. At runtime, our method first finds the nearby exemplars of an input real face and then constructs the result cartoon face from the corresponding cartoon faces of the nearby real face exemplars and the local deformations mapped from the real face subspace. Our method greatly simplifies the cartoon generation process by learning artistic styles from a sparse set of exemplars. We validate the efficiency and effectiveness of our method by applying it to faces of different facial features. Results demonstrate that our method not only preserves the artistic style of the exemplars, but also keeps the unique facial geometric features of different identities.
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This paper describes a 3D face modeling technique that automatically transforms a normal human face to an artistic face. Starting from any 3D human face, the selected 3D artistic-style is gradually added to the face to transform it toward the plausible artistic face. In the transformation process, the human facial identity will remain recognizable while the artistic-style is slowly visible. The main contribution of this work is in the identity preservation algorithm that can prevent the human identity to be altered by the added artistic-style. Our numerical evaluations illustrate that our technique can significantly maintain the facial identity in the transformation process better than other transformation techniques.
This paper proposed a novel approach to generate face caricatures automatically from a single portrait image. We decompose the process of 3D face caricatures generation into two independent subtasks: appearance transfer of texture and the geometry transfer of mesh. For the appearance transfer, we design a GAN-based network named CariFaceGAN to learn the style mapping from portrait to caricature, in which facial features are leveraged to preserve identity consistency. For geometry transfer, we first learn the transformation of the landmarks between portraits and caricatures in an embedded space obtained with Locally Linear Embedding method, and then Kriging interpolation is used to manipulate the portrait mesh constructed from a single image. The experimental results show that our proposed CariFaceGAN outperforms the state-of-the-art methods in terms of maintaining identity consistency and providing satisfactory visual effects.
