突破驾驶世界模型时间限制，实现超2分钟高保真视频生成
InfinityDrive: Breaking Time Limits in Driving World Models
- 采用时空联合建模与扩展时序训练策略
- 生成超过1500帧（超2分钟）保持时空一致的高清视频
- 适合自动驾驶场景模拟与复杂决策测试
自动驾驶系统在复杂场景中表现受限，主要因缺乏多样、广泛且分布外的驾驶数据。世界模型为此提供可行方案，但现有驾驶世界模型受限于短时窗口与场景多样性不足。为此，我们提出InfinityDrive，首个具备卓越泛化能力的驾驶世界模型，实现分钟级视频生成，在高保真度、一致性与多样性上达到当前最佳水平。InfinityDrive引入高效的时空联合建模模块与扩展时序训练策略，支持576×1024分辨率视频生成，保持空间与时间的一致性。通过记忆注入与保留机制，结合自适应记忆曲线损失函数，有效抑制累积误差，实现持续超过1500帧（超过2分钟）的稳定视频生成。多数据集实验证明其能生成复杂多样的驾驶场景，展现其作为下一代自动驾驶世界模型的巨大潜力。
原文摘要 · Abstract (English)
Autonomous driving systems struggle with complex scenarios due to limited access to diverse, extensive, and out-of-distribution driving data which are critical for safe navigation. World models offer a promising solution to this challenge; however, current driving world models are constrained by short time windows and limited scenario diversity. To bridge this gap, we introduce InfinityDrive, the first driving world model with exceptional generalization capabilities, delivering state-of-the-art performance in high fidelity, consistency, and diversity with minute-scale video generation. InfinityDrive introduces an efficient spatio-temporal co-modeling module paired with an extended temporal training strategy, enabling high-resolution (576$\times$1024) video generation with consistent spatial and temporal coherence. By incorporating memory injection and retention mechanisms alongside an adaptive memory curve loss to minimize cumulative errors, achieving consistent video generation lasting over 1500 frames (more than 2 minutes). Comprehensive experiments in multiple datasets validate InfinityDrive's ability to generate complex and varied scenarios, highlighting its potential as a next-generation driving world model built for the evolving demands of autonomous driving. Our project homepage: https://metadrivescape.github.io/papers_project/InfinityDrive/page.html
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