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ARXIV:2603.25730 · VIDEO TECHNOLOGY · SUBMITTED 27 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.25730VIDEO TECHNOLOGYSUBMITTED 27 MAR · 20:30 UTCFRESHNESS STALEXiaofeng Mao · Shaohao Rui · Kaining Ying · Bo Zheng · Chuanhao Li · Mingmin Chi · +1 at arXiv
PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence.
Opportunity summary
Pain PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel…
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global…
Video Technology moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence.
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10.48550/arXiv.2603.25730PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence.
Abstract
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-partition KV-cache strategy. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual-branch network fusing progressive 3D convolutions with low-resolution VAE re-encoding; and (3) Recent tokens, kept at full resolution to ensure local temporal coherence. To strictly bound the memory footprint without sacrificing quality, we introduce a dynamic top-$k$ context selection mechanism for the mid tokens, coupled with a continuous Temporal RoPE Adjustment that seamlessly re-aligns position gaps caused by dropped tokens with negligible overhead. Empowered by this principled hierarchical context compression, PackForcing can generate coherent 2-minute, 832x480 videos at 16 FPS on a single H200 GPU. It achieves a bounded KV cache of just 4 GB and enables a remarkable 24x temporal extrapolation (5s to 120s), operating effectively either zero-shot or trained on merely 5-second clips. Extensive results on VBench demonstrate state-of-the-art temporal consistency (26.07) and dynamic degree (56.25), proving that short-video supervision is sufficient for high-quality, long-video synthesis. https://github.com/ShandaAI/PackForcing
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Dimensions overall score 8.0
PROBLEM
PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-p...
METHOD
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unifi...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which ach...
WHY NOW
Video Technology moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual-branch network fusing progressive 3D convolutions with low-resolution VAE re-encoding
Directly stated in abstract with specific numeric compression factor
partial
PackForcing can generate coherent 2-minute, 832x480 videos at 16 FPS on a single H200 GPU
Directly stated in abstract with specific technical specifications
partial
It achieves a bounded KV cache of just 4 GB
Directly stated in abstract with specific memory measurement
partial
enables a remarkable 24x temporal extrapolation (5s to 120s)
Directly stated in abstract with specific extrapolation factor
partial
Extensive results on VBench demonstrate state-of-the-art temporal consistency (26.07) and dynamic degree (56.25)
Directly stated in abstract with specific benchmark scores
partial
operating effectively either zero-shot or trained on merely 5-second clips
Directly stated in abstract but requires inference that 'short-video supervision' refers to 5-second clips
partial
we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-partition KV-cache strategy
Directly stated in abstract and analysis with clear technical description
partial
The technology might face challenges in generalizing to all types of video content, especially highly complex or specialized genres
Stated in analysis caveats but presented as potential limitation rather than demonstrated finding
partial
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PackForcing: Efficient long-video generation using short-video training with reduced memory footprint and improved temporal coherence.
Segment
Video Technology
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