Training Data Preprocessing For Text To Video Models

In this article, author Aleksandr Rezanov discusses the data preparation for generative text-to-image models to accelerate work on video generation services to be used in TV series and films. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/training-data-preprocessing-for-text-to-video-models/).

What Happened

InfoQ Homepage Articles Training Data Preprocessing for Text-to-Video Models

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).

Filtering removes broken, duplicate, or low-quality clips and weak captions. Classical CV methods (blur detection, lighting checks, optical flow) are combined with embedding-based and text-based approaches (VJEPA, BERT, TF-IDF).

*The opinions expressed are the author’s own and

Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.