![]() ![]() Evaluated on standard benchmark datasets, MaskSketch outperforms state-of-the-art methods for sketch-to-image translation, as well as unpaired image-to-image translation approaches. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation. MaskSketch utilizes a pre-trained masked generative transformer, requiring no model training or paired supervision, and works with input sketches of different levels of abstraction. In this paper, we introduce MaskSketch, an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. However, the majority of these methods allow conditioning only on labels or text prompts, which limits their level of control over the generation result. Recent conditional image generation methods produce images of remarkable diversity, fidelity and realism. Our usability study verifies that our system is greatly preferred by user. Extensive comparisons show that our method generates high-quality results that match the sketch. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. Finally, we discuss some existing challenges and suggest possible future research directions.Ĭreating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Moreover, a detailed analysis based on the evaluation metrics of the results of the introduced image synthesis is provided. Regarding the evaluation, we summarize the metrics used to evaluate the image synthesis models. Next, brief details of the benchmarked datasets used in image synthesis are discussed along with specifying the image synthesis models that leverage them. Each sub-category is introduced under the proper category based upon the general framework to provide a broad vision of all existing image synthesis methods. We then review different image synthesis methods divided into three categories: image generation from text, sketch, and other inputs, respectively. First, the image synthesis concept is introduced. Thus, the aim of this paper is to provide a comprehensive review of various image synthesis models covering several aspects. ![]() Different techniques and strategies have been employed to achieve this purpose. It is an important problem in the computer vision field, where it has attracted the research community to attempt to solve this challenge at a high level to generate photorealistic images. ![]() ![]() Image synthesis is a process of converting the input text, sketch, or other sources, i.e., another image or mask, into an image. ![]()
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