标题： 长时域驾驶世界模型：跨粒度蒸馏方法 显示英文标题

标题： LongDWM: Cross-Granularity Distillation for Building a Long-Term Driving World Model

摘要： 基于当前状态和动作，驱动世界模型用于通过视频生成来模拟未来。然而，当前的模型在预测长期未来时往往会出现严重的误差累积，这限制了它们的实际应用。近期的研究利用扩散变换器（Diffusion Transformer，DiT）作为驱动世界模型的主干以提高学习灵活性。然而，这些模型通常是在短小的视频片段（高帧率且时间较短）上进行训练的，在推理过程中，由于训练与推理之间的差距，多次展开生成难以产生一致且合理的长视频。 为此，我们提出了一些解决方案，构建了一个简单但有效的长期驱动世界模型。首先，我们将世界模型的学习分层解耦为大范围运动学习和双向连续运动学习。然后，考虑到驾驶场景的连续性，我们提出了一种简单的蒸馏方法，其中精细的视频流作为粗粒度流的自监督信号。该蒸馏设计旨在提升无限视频生成的一致性。粗粒度模块和细粒度模块协同工作，生成长时间且时间一致的视频。 在公共基准数据集NuScenes上，与最先进的前视图模型相比，我们的模型在生成110多帧的视频任务中，FVD（Fréchet Video Distance）提高了$27\%$，推理时间减少了$85\%$。更多视频（包括90秒时长）可在https://Wang-Xiaodong1899.github.io/longdwm/获取。 显示英文摘要

摘要： Driving world models are used to simulate futures by video generation based on the condition of the current state and actions. However, current models often suffer serious error accumulations when predicting the long-term future, which limits the practical application. Recent studies utilize the Diffusion Transformer (DiT) as the backbone of driving world models to improve learning flexibility. However, these models are always trained on short video clips (high fps and short duration), and multiple roll-out generations struggle to produce consistent and reasonable long videos due to the training-inference gap. To this end, we propose several solutions to build a simple yet effective long-term driving world model. First, we hierarchically decouple world model learning into large motion learning and bidirectional continuous motion learning. Then, considering the continuity of driving scenes, we propose a simple distillation method where fine-grained video flows are self-supervised signals for coarse-grained flows. The distillation is designed to improve the coherence of infinite video generation. The coarse-grained and fine-grained modules are coordinated to generate long-term and temporally coherent videos. In the public benchmark NuScenes, compared with the state-of-the-art front-view model, our model improves FVD by $27\%$ and reduces inference time by $85\%$ for the video task of generating 110+ frames. More videos (including 90s duration) are available at https://Wang-Xiaodong1899.github.io/longdwm/.