Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation
Sketching is an intuitive and effective way for content creation. While significant progress has been made for photorealistic image generation by using generative adversarial networks, it remains challenging to take a fine-grained control on synthetic content. The instance normalization layer, which is widely adopted in existing image translation networks, washes away details in the input sketch and leads to loss of precise control on the desired shape of the generated face images. In this paper, we comprehensively investigate the effect of instance normalization on generating photorealistic face images from hand-drawn sketches. We first introduce a visualization approach to analyze the feature embedding for sketches with a group of specific changes. Based on the visual analysis, we modify the instance normalization layers in the baseline image translation model. We elaborate a new set of hand-drawn sketches with 11 categories of specially designed changes and conduct extensive experimental analysis. The results and user studies demonstrate that our method markedly improve the quality of synthesized images and the conformance with user intention.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Disentangled GANs for Controllable Generation of High-Resolution Images
Generative adversarial networks (GANs) have achieved great success at generating realistic samples. However, achieving disentangled and controllable generation still remains challenging for GANs, especially in the high-r…
DisentanglementVocal Bursts Intensity PredictionOne-Shot Fine-Grained Instance Retrieval
Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recogni…
Fine-Grained Visual CategorizationImage RetrievalRetrievalALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search e…
AllRepresentation LearningDRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis
In recent years, conditional image synthesis has attracted growing attention due to its controllability in the image generation process. Although recent works have achieved realistic results, most of them have difficulty…
Facial Makeup TransferImage GenerationStyle TransferTexture SynthesisConFusion: Continuous Fusion Space Learning for Fine-Grained Controllable Infrared and Visible Image Fusion
Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that adapt to diverse user requirements and down…