Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation

2026-07-07 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

PACR-Video introduces a parameter-efficient framework for multi-shot long video extrapolation, designed to preserve recurring entities, scene structure, visual style, and causal progression without full generator fine-tuning. It operates by keeping a text-to-video diffusion transformer frozen, augmenting it with low-rank temporal adapters conditioned by learned shot-role prompt tokens. To maintain long-horizon coherence, the framework builds a recursive prompt bank that stores compact entity, location, action, and style prompts from previous shots, routing them through adapter gates based on predicted narrative dependencies. A Shot-Local/Story-Global tuning objective, combined with an adapter composition schedule, balances early-shot visual consistency with later-shot event progression. PACR-Video significantly outperforms various baselines across six benchmarks in distributional quality, semantic alignment, identity consistency, and temporal smoothness.

Key takeaway

For ML engineers developing long-form video generation systems, PACR-Video offers a parameter-efficient approach to maintain coherence across multiple shots. You should consider integrating prompt routing and low-rank temporal adapters to preserve entity identity and scene structure without extensive fine-tuning. This method can significantly improve temporal consistency and visual quality in your generated long videos, making it a valuable technique for complex video synthesis tasks.

Key insights

Compact prompt routing and lightweight temporal adaptation enable stable long video extrapolation.

Principles

• Freeze core generator, augment with low-rank adapters.
• Maintain coherence via recursive prompt banks.
• Balance consistency and progression with adaptive schedules.

Method

PACR-Video uses a frozen text-to-video diffusion transformer, augmenting it with low-rank temporal adapters conditioned by learned shot-role prompt tokens and a recursive prompt bank for context routing.

In practice

• Generate multi-shot videos with consistent entities.
• Extrapolate long videos while preserving style.
• Improve temporal coherence in generated sequences.

Topics

• Video Extrapolation
• Diffusion Transformers
• Parameter-Efficient Learning
• Prompt Engineering
• Temporal Adapters
• Long Video Generation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.