Pack and Force Your Memory: Long-form and Consistent Video Generation

• URL: http://arxiv.org/abs/2510.01784v2
• Date: Fri, 03 Oct 2025 16:01:28 GMT
• Title: Pack and Force Your Memory: Long-form and Consistent Video Generation
• Authors: Xiaofei Wu, Guozhen Zhang, Zhiyong Xu, Yuan Zhou, Qinglin Lu, Xuming He,
• Abstract summary: Long-form video generation presents a dual challenge.<n>Models must capture long-range dependencies while preventing the error accumulation inherent in autoregressive decoding.<n>MemoryPack and Direct Forcing substantially enhance the context consistency and reliability of long-form video generation.
• Score: 26.53691150499802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-form video generation presents a dual challenge: models must capture long-range dependencies while preventing the error accumulation inherent in autoregressive decoding. To address these challenges, we make two contributions. First, for dynamic context modeling, we propose MemoryPack, a learnable context-retrieval mechanism that leverages both textual and image information as global guidance to jointly model short- and long-term dependencies, achieving minute-level temporal consistency. This design scales gracefully with video length, preserves computational efficiency, and maintains linear complexity. Second, to mitigate error accumulation, we introduce Direct Forcing, an efficient single-step approximating strategy that improves training-inference alignment and thereby curtails error propagation during inference. Together, MemoryPack and Direct Forcing substantially enhance the context consistency and reliability of long-form video generation, advancing the practical usability of autoregressive video models.

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