ChatGPT Generates Animated GIFs from Text and Photos

OpenAI has introduced a new image generation model called 'Images 2.6' to ChatGPT, adding the ability to create animated GIFs from text instructions or photos. Users can now easily generate moving images by simply entering a prompt or uploading a photo.

GIF is an image format that displays multiple still images in sequence like a flipbook to create the appearance of movement, and is widely used on social media and messaging apps. Until now, creating animated GIFs manually required specialized software or a certain level of technical knowledge. With this new feature addition, a general user without technical barriers can now create moving images starting from text or photos.

With Images 2.6, users can also adjust the playback speed of the animation after generation and edit individual frames. Additionally, image consistency has been enhanced to ensure that character appearances remain consistent across frames. This design enables animation styles similar to flipbook manga, which expresses movement through consecutive frames.

According to OpenAI, ChatGPT users currently generate over 4 billion images per week. This figure demonstrates the scale at which AI-generated images have already become established as everyday tools. ChatGPT's functionality, which began with text generation, has steadily expanded through still image generation into video and animation domains.

The significance of this feature particularly lies in its ability to significantly lower the barrier to entry for 'creating moving images'. Work that creators and designers have previously invested considerable time in may now be completed with a single line of text, potentially transforming the shape of content production. On the other hand, the extent to which GIF quality and range of expression can meet practical needs largely depends on evaluation through actual use.

AI-powered image and video generation is becoming a major competitive domain alongside text-generating AI. Going forward, attention is expected to focus on directions such as the length and complexity of generatable animations, or support for video formats.