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Recent advances in the user evaluation methods and studies of non-photorealistic visualisation and rendering techniques
MW-GAN: Multi-warping GAN for caricature generation with multi-style geometric exaggeration
Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous style transfer and shape exaggeration with rich diversity, and meanwhile preserving the identity of the input. To address this challenging problem, we propose a novel framework called Multi-Warping GAN (MW-GAN), including a style network and a geometric network that are designed to conduct style transfer and geometric exaggeration respectively. We bridge the gap between the style/landmark space and their corresponding latent code spaces by a dual way design, so as to generate caricatures with arbitrary styles and geometric exaggeration, which can be specified either through random sampling of latent code or from a given caricature sample. Besides, we apply identity preserving loss to both image space and landmark space, leading to a great improvement in quality of generated caricatures. Experiments show that caricatures generated by MW-GAN have better quality than existing methods
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars
Modern 3D-GANs synthesize geometry and texture by training on large-scale
datasets with a consistent structure. Training such models on stylized,
artistic data, with often unknown, highly variable geometry, and camera
information has not yet been shown possible. Can we train a 3D GAN on such
artistic data, while maintaining multi-view consistency and texture quality? To
this end, we propose an adaptation framework, where the source domain is a
pre-trained 3D-GAN, while the target domain is a 2D-GAN trained on artistic
datasets. We then distill the knowledge from a 2D generator to the source 3D
generator. To do that, we first propose an optimization-based method to align
the distributions of camera parameters across domains. Second, we propose
regularizations necessary to learn high-quality texture, while avoiding
degenerate geometric solutions, such as flat shapes. Third, we show a
deformation-based technique for modeling exaggerated geometry of artistic
domains, enabling -- as a byproduct -- personalized geometric editing. Finally,
we propose a novel inversion method for 3D-GANs linking the latent spaces of
the source and the target domains. Our contributions -- for the first time --
allow for the generation, editing, and animation of personalized artistic 3D
avatars on artistic datasets.Comment: Project Page: https://rameenabdal.github.io/3DAvatarGAN
RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization
Robotic drawing has become increasingly popular as an entertainment and
interactive tool. In this paper we present RoboCoDraw, a real-time
collaborative robot-based drawing system that draws stylized human face
sketches interactively in front of human users, by using the Generative
Adversarial Network (GAN)-based style transfer and a Random-Key Genetic
Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes
a real human face image as input, converts it to a stylized avatar, then draws
it with a robotic arm. A core component in this system is the Avatar-GAN
proposed by us, which generates a cartoon avatar face image from a real human
face. AvatarGAN is trained with unpaired face and avatar images only and can
generate avatar images of much better likeness with human face images in
comparison with the vanilla CycleGAN. After the avatar image is generated, it
is fed to a line extraction algorithm and converted to sketches. An RKGA-based
path optimization algorithm is applied to find a time-efficient robotic drawing
path to be executed by the robotic arm. We demonstrate the capability of
RoboCoDraw on various face images using a lightweight, safe collaborative robot
UR5.Comment: Accepted by AAAI202
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