Text-to-Video AI (Video Generation)
Text-to-video AI generates self-contained video clips from plain text descriptions (prompts), including camera movement, object physics and increasingly synchronised sound such as speech, effects and music. Technically, as with text-to-image AI, these are diffusion and transformer architectures extended to hold temporal coherence between individual frames. Which clip lengths, resolutions and models are actually available changes constantly and has to be taken from the provider documentation.
In practice
For content marketing and social commerce, text-to-video AI makes it possible to produce social media clips, product videos or advertising teasers quickly and cheaply without a camera crew or a set. For SMEs in Austria and Germany, it is worth using mainly for short formats on TikTok, Instagram Reels or product announcements, where speed and volume of testing matter more than cinematic perfection. As of 14 August 2026, the OpenAI documentation lists clips of 16 or 20 seconds for the models sora-2 and sora-2-pro and recommends sora-2-pro for 1920x1080 or 1080x1920 exports, while cheaper drafts are rendered at 720p or 480p. As of the same date, the Gemini API documentation treats Gemini Omni Flash as the default model for video generation and points to Veo 3.1 only for specific capabilities such as scene extension, last-frame control or integration with legacy pipelines. These details date quickly – check the current provider documentation before planning. The Sora app is only available to a limited extent in Austria and Germany (as of 14 August 2026); access usually runs through ChatGPT Plus/Pro or the API, while on the Google side Gemini and Google Flow offer an easier entry point. Legally, the transparency duties in Article 50 of the EU AI Act are the relevant ones, particularly where people or voices look realistic (deepfake risk) – which of them apply depends on whether a company acts as a provider or as a deployer; Content Credentials and watermarks help with transparency and traceability. Before publication, every video should be checked for factual errors, unwanted artefacts and brand compliance, because physics and on-screen text in generated video can still be error-prone.