DataPack
Camera Controls
Teach models how cameras actually behave — movement, framing, lens behavior, rig dynamics, depth of field, and composition.
Solution
Generative video models are improving fast, but still break on consistency — lighting, camera movement, object continuity, lip sync, and coherent action across shots.
Flikforge engineers training data that closes those gaps — structured, rights-cleared, GPU-ready.
Browse Relevant DataPacks →
The Core Problem
Generative video models train on raw footage with no schema for the decisions that govern coherent output — camera behavior, lighting continuity, shot-to-shot continuity, and scene state. They learn implicitly and plateau. Closing that gap requires structured signal, not more footage.
Performance Gaps Addressed
Models confuse pan with tilt, drift on static shots, and struggle to reproduce handheld feel without structured camera-motion, lens, rig, and framing metadata.
Characters, objects, lighting, and spatial relationships drift between cuts when training data lacks shot-to-shot continuity and scene-level structure.
Shots are treated as independent frames instead of successive states. Models need state-change and sequence annotations to track what changes over time.
Models hallucinate light sources, miss directional cues, and confuse hard with soft light without structured lighting metadata across source type, directionality, exposure, and mood.
Relevant DataPacks
Raw video doesn’t teach camera grammar, lighting logic, or shot continuity. These DataPacks deliver the structured cinematic signal generative video teams can’t get from generic pipelines — camera behavior, scene continuity, temporal state, and lighting dynamics.
Need a different use case? View the full DataPack library →
DataPack
Teach models how cameras actually behave — movement, framing, lens behavior, rig dynamics, depth of field, and composition.
DataPack
Train models on cinematic lighting behavior, from source direction and exposure shifts to dramatic key setups, practicals, specialty effects, and scene-level illumination changes.
DataPack
Strengthen model reasoning around scene composition, object relationships, environmental context, and what’s happening across space and time.
DataPack
Help models understand environmental context, object state changes, temporal continuity, and the semantic cues that make scenes coherent.