Post by Turing

Teaching AI to think like an ad director is harder than it sounds. When a multimodal model needs to generate product video concepts from image inputs, it can't just describe what it sees. It has to understand advertising logic: how a wide establishing shot gives way to a feature close-up, how a voice-over line earns credibility by tying directly to what's on screen, and how 15 sequential shots build a coherent brand story in under two minutes. That's the dataset Turing just delivered. 500+ structured ad storyboard tasks. 7,500+ original shot descriptions, each grounded in real product images and descriptions with zero invented features. 6,000+ voice-over lines matched to specific product benefits visible in the corresponding shot. 90%+ quality score maintained across the full dataset. Every shot was built around a defined advertising arc, from product introduction through feature demonstration, lifestyle relevance, and brand reinforcement. Camera motion was specified from a standardized taxonomy covering static, pan, tilt, tracking, arc, and handheld directions. Quality was enforced through a four-dimension rubric covering conceptual accuracy, visual creativity, ad structure, and technical adherence, with rework required before delivery. Storyboards were also tested against SOTA video generation models including VEO and Seedance to verify that descriptions translated into coherent video sequences without hallucinations or generation artifacts. If you're building or evaluating multimodal models for product video generation, this is the kind of training data that makes the difference between a model that describes products and one that understands how to sell them. Read the full case study: https://lnkd.in/e6vBBXhx