[Paper] Context Forcing: Consistent Autoregressive Video Generation with Long Context

Source: arXiv - 2602.06028v1

Overview

The paper introduces Context Forcing, a training framework that lets autoregressive video generators keep a coherent story over much longer periods than before. By teaching a “student” model with a teacher that can see the entire generation history, the authors eliminate a long‑standing mismatch that limited context length to only a few seconds. The result is video synthesis that stays consistent for 20 seconds – 2 minutes, far surpassing existing real‑time generators.

Key Contributions

• Long‑context teacher–student paradigm: Replaces the conventional short‑window teacher with a teacher that has access to the full video history, removing the student‑teacher supervision gap.
• Context Forcing loss: A novel objective that forces the student to match the teacher’s predictions while the teacher is conditioned on the entire past context.
• Slow‑Fast Memory architecture: A context‑management system that compresses redundant visual information, turning a linearly growing context into a scalable “slow‑fast” memory bank.
• Empirical breakthroughs: Demonstrates consistent generation up to >20 s (and up to 2 min in experiments), 2–10× longer than prior state‑of‑the‑art methods such as LongLive and Infinite‑RoPE.
• Comprehensive evaluation: Introduces and reports on several long‑video metrics (temporal consistency, motion smoothness, semantic drift) showing clear gains over baselines.

Methodology

1. Teacher‑Student Setup

   • Student: The autoregressive video model that will be deployed for real‑time generation.
   • Teacher: A copy of the same architecture but run offline with access to the full generated sequence (the entire “history”).

2. Context Forcing Training

   • At each timestep, the teacher predicts the next frame using the complete past context.
   • The student predicts the same frame using only the available context (which grows as generation proceeds).
   • A forcing loss (e.g., KL divergence) aligns the student’s distribution with the teacher’s, ensuring the student learns to emulate a model that already knows the long‑range dependencies.

3. Slow‑Fast Memory

   • Fast memory stores recent frames at full resolution for fine‑grained detail.
   • Slow memory aggregates older frames into a compressed representation (e.g., down‑sampled features, key‑frame embeddings).
   • When the context length exceeds a threshold, older fast entries are migrated to slow memory, keeping the overall memory footprint roughly constant while preserving essential temporal cues.

4. Training Pipeline

   • Videos are split into short clips for efficient GPU usage.
   • The teacher runs on the whole clip (or the entire video in a streaming fashion) while the student processes it incrementally.
   • Gradients are back‑propagated only through the student; the teacher’s parameters are frozen after an initial warm‑up.

Results & Findings

• Longer context directly translates into smoother motion and fewer abrupt scene changes.
• Qualitative examples show the model maintaining object identities, lighting conditions, and narrative flow for tens of seconds—something prior models lose after a few seconds.
• Ablation studies confirm that both the teacher’s full‑history access and the Slow‑Fast memory are essential; removing either drops performance to near‑baseline levels.

Practical Implications

• Real‑time content creation: Game engines, virtual production, or live‑stream overlays can now generate background animations that stay coherent for extended periods without pre‑rendering.
• Extended AR/VR experiences: Users can interact with AI‑generated environments that evolve naturally over minutes, improving immersion.
• Data‑efficient video synthesis: The Slow‑Fast memory reduces GPU memory usage, enabling deployment on consumer‑grade hardware (e.g., RTX 30‑series) for longer clips.
• Improved video‑to‑video translation: When adapting a source video (e.g., style transfer), the longer context helps preserve scene continuity, reducing flicker.
• Foundation model fine‑tuning: The teacher‑student paradigm can be repurposed for other sequential generative tasks (audio, text) where long‑range consistency matters.

Limitations & Future Work

• Training cost: Running a full‑context teacher alongside the student still requires substantial GPU hours, especially for minutes‑long videos.
• Memory compression trade‑off: The Slow‑Fast scheme may discard subtle long‑term cues; future work could explore learnable compression or hierarchical attention.
• Domain generalization: Experiments focus on relatively controlled datasets (e.g., human motion, synthetic scenes). Extending to highly dynamic, outdoor footage remains an open challenge.
• Interactive control: The current setup assumes unconditional generation; integrating user‑driven constraints (e.g., keyframe editing) is a promising direction.

Context Forcing pushes the frontier of autoregressive video generation from “short bursts” to truly long‑form synthesis, opening up new possibilities for developers building interactive media, real‑time visual effects, and AI‑driven content pipelines.

Authors

• Shuo Chen
• Cong Wei
• Sun Sun
• Ping Nie
• Kai Zhou
• Ge Zhang
• Ming-Hsuan Yang
• Wenhu Chen

Paper Information

• arXiv ID: 2602.06028v1
• Categories: cs.CV
• Published: February 5, 2026
• PDF: Download PDF