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This is a list of emoticons or textual portrayals of a writer's moods or facial expressions in the form of icons. Originally, these icons consisted of ASCII art, and later, Shift JIS art and Unicode art. In recent times, graphical icons, both static and animated, have joined the traditional text-based emoticons; these are commonly known as ...
A facial expression database is a collection of images or video clips with facial expressions of a range of emotions. Well-annotated ( emotion -tagged) media content of facial behavior is essential for training, testing, and validation of algorithms for the development of expression recognition systems .
Kaomoji was invented in the 1980s as a way of portraying facial expressions using text characters in Japan. It was independent of the emoticon movement started by Scott Fahlman in the United States in the same decade. Kaomojis are most commonly used as emoticons or emojis in Japan.
The Emoji application for iOS, which altered the Settings app to allow access to the emoji keyboard, was created by Josh Gare in February 2010. [62] Before the existence of Gare's Emoji app, Apple had intended for the emoji keyboard to only be available in Japan in iOS version 2.2.
Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.
A different, but related, use of the term "emoticon" is found in the Unicode Standard, referring to a subset of emoji that display facial expressions. [74] The standard explains this usage with reference to existing systems, which provided functionality for substituting certain textual emoticons with images or emoji of the expressions in question.
Afrikaans; العربية; Aragonés; বাংলা; Беларуская (тарашкевіца) Български; Чӑвашла; Deutsch; Español; فارسی
Ideogram is a freemium text-to-image model developed by Ideogram, Inc. using deep learning methodologies to generate digital images from natural language descriptions known as prompts. The model is capable of generating legible text in the images compared to other text-to-image models. [1] [2]
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