Training Data Preprocessing for Text-to-Video Models

"Text-to-video models (Runway, Sora, Veo 3, Pika, Luma) are trained on large datasets of video-text pairs, and data quality directly determines generation quality ("garbage in, garbage out"). Assembling and preprocessing such datasets is at the core of the text-to-video generation business case. The preprocessing pipeline consists of three main stages - scene splitting, video labeling, and filtering. Each of them addresses a specific problem: clips that are too long, lacking captions, and low-quality or broken samples."

"Scene splitting prepares long raw videos for training by cutting them into short, coherent clips. Tools like ffmpeg, PySceneDetector, and OpenCV are used; embeddings (e.g., ImageBind) can help merge semantically connected fragments. Video labeling assigns each clip a concise text description. Manual labeling can define quality standards, while large-scale captioning is done with visual-language models and APIs (Transformers, CogVLM2-Video, OpenAI, Gemini)."

"With the growing interest in generative AI services such as Runway Gen-2, Pika Labs, Luma AI, which create visual content based on user prompts, the technology is moving beyond experimental projects and finding its place in production workflows. These solutions, built on deep neural models, are being adopted both by companies offering video generation as a service and in production pipelines - accelerating work on TV series and films and powering the creation of ad campaigns."

High-quality video-text datasets determine model output quality and underpin text-to-video generation business value. Assembling and preprocessing pipelines include scene splitting, video labeling, and filtering to address long clips, missing captions, and low-quality samples. Scene splitting cuts long raw videos into short coherent clips using tools like ffmpeg, PySceneDetector, and OpenCV; embeddings such as ImageBind help merge semantically connected fragments. Video labeling assigns concise captions via manual annotation for quality control and automated large-scale captioning with visual-language models and APIs (Transformers, CogVLM2-Video, OpenAI, Gemini). Filtering removes broken, duplicate, or low-quality clips and weak captions using classical CV checks (blur, lighting, optical flow) combined with embedding-based and text-based methods (VJEPA, BERT, TF-IDF). Diffusion-based text-to-video models are being adopted in production to accelerate content creation for TV, film, and advertising.

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