# CaricatureShop: Personalized and Photorealistic Caricature Sketching

arXiv: https://arxiv.org/abs/1807.09064 · HTML: https://mlsanity.com/paper/1807.09064/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2018.

## Built on by (most cited first, 12 of 12)

- [SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text](https://mlsanity.com/paper/2204.11964/) (2022)
- [Exemplar-Based 3D Portrait Stylization](https://mlsanity.com/paper/2104.14559/) (2021)
- [Deep Deformable 3D Caricatures with Learned Shape Control](https://mlsanity.com/paper/2207.14593/) (2022)
- [AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging](https://mlsanity.com/paper/2211.07818/) (2022)
- [StyleCariGAN: Caricature Generation via StyleGAN Feature Map Modulation](https://mlsanity.com/paper/2107.04331/) (2021)
- [FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing](https://mlsanity.com/paper/2412.19009/) (2024)
- [Tag-based annotation creates better avatars](https://mlsanity.com/paper/2302.07354/) (2023)
- [3D Face Arbitrary Style Transfer](https://mlsanity.com/paper/2303.07709/) (2023)
- [3Deformer: A Common Framework for Image-Guided Mesh Deformation](https://mlsanity.com/paper/2307.09892/) (2023)
- [High-Quality Face Caricature via Style Translation](https://mlsanity.com/paper/2311.13338/) (2023)
- [CaricatureGS: Exaggerating 3D Gaussian Splatting Faces With Gaussian Curvature](https://mlsanity.com/paper/2601.03319/) (2026)
- [SketchFaceGS: Real-Time Sketch-Driven Face Editing and Generation with Gaussian Splatting](https://mlsanity.com/paper/2604.19202/) (2026)

## Abstract (arXiv)

In this paper, we propose the first sketching system for interactively personalized and photorealistic face caricaturing. Input an image of a human face, the users can create caricature photos by manipulating its facial feature curves. Our system firstly performs exaggeration on the recovered 3D face model according to the edited sketches, which is conducted by assigning the laplacian of each vertex a scaling factor. To construct the mapping between 2D sketches and a vertex-wise scaling field, a novel deep learning architecture is developed. With the obtained 3D caricature model, two images are generated, one obtained by applying 2D warping guided by the underlying 3D mesh deformation and the other obtained by re-rendering the deformed 3D textured model. These two images are then seamlessly integrated to produce our final output. Due to the severely stretching of meshes, the rendered texture is of blurry appearances. A deep learning approach is exploited to infer the missing details for enhancing these blurry regions. Moreover, a relighting operation is invented to further improve the photorealism of the result. Both quantitative and qualitative experiment results validated the efficiency of our sketching system and the superiority of our proposed techniques against existing methods.