Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches
By Weihao Xia, Yujiu Yang, Jing-Hao Xue. 2019
15 citations recorded by the source corpus.
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Citations in the corpus
- UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis (2021, background): “With the increasing demand for flexible conditional image synthesis, various kinds of control signals have been introduced into this field, which can be divided into three main modalities: (i) textual controls (TC), including the class labels [1] and natural language descriptions [62, 54]; (ii) visu”
- TediGAN: Text-Guided Diverse Face Image Generation and Manipulation (2021, background): “To make image generation and manipulation more readily and user-friendly, recent studies have been focusing on the image synthesis conditioned on a variety of guidance, such as sketch [9, 37], semantic label [11, 36], or textual description [26, 39].”
- TediGAN: Text-Guided Diverse Image Generation and Manipulation (2020, background): “To make image generation and manipulation more readily and user-friendly, recent studies have been focusing on the image synthesis conditioned on a variety of guidance, such as sketch [12, 51], semantic label [15, 49], or textual description [35, 52].”
- Towards Open-World Text-Guided Face Image Generation and Manipulation (2021, background): “Both sketch-based and label-based categories put forward high requirements for the user’s drawing [28].”
- M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers (2021, background): “The image-to-image translation task generates photo-realism images from visual controls, such as a sketch map [17, 61], semantic label map [26, 4, 59], human pose [38] or another image for style transfer [15, 27].”