Caricature
18 papers with code • 0 benchmarks • 1 datasets
Caricature is a pictorial representation or description that deliberately exaggerates a person’s distinctive features or peculiarities to create an easily identifiable visual likeness with a comic effect. This vivid art form contains the concepts of abstraction, simplification and exaggeration.
Source: Alive Caricature from 2D to 3D
Benchmarks
These leaderboards are used to track progress in Caricature
WebCaricature Dataset
Most implemented papers
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
The implementation of the proposed method is available at https://github. com/KeleiHe/DAAN.
Learning to Caricature via Semantic Shape Transform
wenqingchu/Semantic-CariGANs
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Caricature is an artistic drawing created to abstract or exaggerate facial features of a person.
StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation
Our framework, dubbed StyleCariGAN, automatically creates a realistic and detailed caricature from an input photo with optional controls on shape exaggeration degree and color stylization type.
Dual-Domain Image Synthesis using Segmentation-Guided GAN
denabazazian/Dual-Domain-Synthesis
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We build on the successes of few-shot StyleGAN and single-shot semantic segmentation to minimise the amount of training required in utilising two domains.
Deep Deformable 3D Caricatures with Learned Shape Control
ycjungsubhuman/deepdeformable3dcaricatures
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To achieve the goal, we propose an MLP-based framework for building a deformable surface model, which takes a latent code and produces a 3D surface.
Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciences
A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data.
CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations
Recent work has aimed to capture nuances of human behavior by using LLMs to simulate responses from particular demographics in settings like social science experiments and public opinion surveys.
Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predictions.
