- Updated: March 10, 2026
- 1 min read
Screen, Match, and Cache: A Training‑Free Causality‑Consistent Reference Frame Framework for Human Animation
Human animation aims to generate temporally coherent and visually consistent videos over long sequences. In this article we present the FrameCache framework, a training‑free three‑stage approach consisting of Screen, Cache, and Match. The Screen stage dynamically selects informative frames using a quality‑aware mechanism with adaptive thresholds. The Cache stage maintains a reference pool with a dynamic replacement‑hit strategy to preserve diversity and relevance. The Match stage extracts behavioral features to perform motion‑consistent reference matching, guiding coherent animation.
Extensive experiments on standard benchmarks demonstrate that FrameCache consistently improves temporal coherence and visual stability while integrating seamlessly with diverse baselines. The framework’s effectiveness depends on baseline temporal reasoning and real‑synthetic consistency, motivating future work on compatibility conditions and adaptive cache mechanisms.
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Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.