Stylizing Face Images via Multiple Exemplars
We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on the target image are inevitably inconsistent. The artifacts are removed by a post-processing step using an edge-preserving filter. Experimental results show that the proposed algorithm consistently produces visually pleasing results.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing
Given a really low-resolution input image of a face (say 16x16 or 8x8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency i…
Super-ResolutionMulGAN: Facial Attribute Editing by Exemplar
Recent studies on face attribute editing by exemplars have achieved promising results due to the increasing power of deep convolutional networks and generative adversarial networks. These methods encode attribute-related…
AttributeStylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization
Deep learning models in medical image analysis often struggle with generalizability across domains and demographic groups due to data heterogeneity and scarcity. Traditional augmentation improves robustness, but fails un…
Domain GeneralizationImage ClassificationData AugmentationStyle TransferHIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
A promising direction for recovering the lost information in low-resolution headshot images is utilizing a set of high-resolution exemplars from the same identity. Complementary images in the reference set can improve th…
Image Super-ResolutionSuper-ResolutionPrompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning
Replay-based methods in class-incremental learning~(CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited number of exemplars with poor diversity.…
class-incremental learningClass Incremental LearningData AugmentationDiversity+1