arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2512.04040cs.CV
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RELIC:具有长时间记忆的交互式视频世界模型

RELIC: Interactive Video World Model with Long-Horizon Memory

Yicong Hong, Yiqun Mei, Chongjian Ge, Yiran Xu, Yang Zhou, Sai Bi, Yannick Hold-Geoffroy, Mike Roberts, Matthew Fisher, Eli Shechtman, Kalyan Sunkavalli, Feng L… 展开作者

Yicong Hong, Yiqun Mei, Chongjian Ge, Yiran Xu, Yang Zhou, Sai Bi, Yannick Hold-Geoffroy, Mike Roberts, Matthew Fisher, Eli Shechtman, Kalyan Sunkavalli, Feng Liu, Zhengqi Li, Hao Tan

AI总结:

RELIC通过统一框架实现长时记忆与实时交互,提升视频世界建模的准确性与稳定性。

摘要:

真正交互式的世界模型需要三个关键要素:实时长时间流媒体、一致的空间记忆以及精确的用户控制。然而,大多数现有方法只解决这三个方面中的一个,因为同时实现这三个方面极具挑战性——例如,长期记忆机制通常会降低实时性能。在本文中,我们提出了RELIC,一个统一的框架,全面解决这三个挑战。给定一张图像和一个文本描述,RELIC能够实时进行任意场景的长时间探索。基于最近的自回归视频扩散蒸馏技术,我们的模型使用高度压缩的历史潜在标记来表示长时间记忆,这些标记通过相对动作和绝对相机姿态在KV缓存中编码。这种紧凑的、相机感知的记忆结构支持隐式的3D一致内容检索,并通过最小的计算开销实现长期的一致性。同时,我们微调了一个双向教师视频模型,以生成超过其原始5秒训练时间范围的序列,并通过一种新的记忆高效自我强制范式将其转换为因果学生生成器,该范式能够实现对长时间教师以及长学生自我滚动的完整上下文蒸馏。作为14B参数模型,并在经过筛选的Unreal Engine渲染数据集上训练,RELIC在16 FPS下实现了实时生成,同时在动作跟随、长时间流媒体稳定性和空间记忆检索方面比先前工作更准确、更稳定和更健壮。这些能力使RELIC成为下一代交互式世界建模的强大基础。

英文摘要:

A truly interactive world model requires three key ingredients: real-time long-horizon streaming, consistent spatial memory, and precise user control. However, most existing approaches address only one of these aspects in isolation, as achieving all three simultaneously is highly challenging-for example, long-term memory mechanisms often degrade real-time performance. In this work, we present RELIC, a unified framework that tackles these three challenges altogether. Given a single image and a text description, RELIC enables memory-aware, long-duration exploration of arbitrary scenes in real time. Built upon recent autoregressive video-diffusion distillation techniques, our model represents long-horizon memory using highly compressed historical latent tokens encoded with both relative actions and absolute camera poses within the KV cache. This compact, camera-aware memory structure supports implicit 3D-consistent content retrieval and enforces long-term coherence with minimal computational overhead. In parallel, we fine-tune a bidirectional teacher video model to generate sequences beyond its original 5-second training horizon, and transform it into a causal student generator using a new memory-efficient self-forcing paradigm that enables full-context distillation over long-duration teacher as well as long student self-rollouts. Implemented as a 14B-parameter model and trained on a curated Unreal Engine-rendered dataset, RELIC achieves real-time generation at 16 FPS while demonstrating more accurate action following, more stable long-horizon streaming, and more robust spatial-memory retrieval compared with prior work. These capabilities establish RELIC as a strong foundation for the next generation of interactive world modeling.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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