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MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving

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arxiv 2503.15875 v1 pith:3OGDTXPJ submitted 2025-03-20 cs.CV

Haiguang Wang , Daqi Liu , Hongwei Xie , Haisong Liu , Enhui Ma , Kaicheng Yu , Limin Wang , Bing Wang This is my paper · ORCID
classification cs.CV
keywords milavideogenerationvideosautonomousdatadenoisingdriving
verification ladder T0 review T1 audit T2 compute T3 formal
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In recent years, data-driven techniques have greatly advanced autonomous driving systems, but the need for rare and diverse training data remains a challenge, requiring significant investment in equipment and labor. World models, which predict and generate future environmental states, offer a promising solution by synthesizing annotated video data for training. However, existing methods struggle to generate long, consistent videos without accumulating errors, especially in dynamic scenes. To address this, we propose MiLA, a novel framework for generating high-fidelity, long-duration videos up to one minute. MiLA utilizes a Coarse-to-Re(fine) approach to both stabilize video generation and correct distortion of dynamic objects. Additionally, we introduce a Temporal Progressive Denoising Scheduler and Joint Denoising and Correcting Flow modules to improve the quality of generated videos. Extensive experiments on the nuScenes dataset show that MiLA achieves state-of-the-art performance in video generation quality. For more information, visit the project website: https://github.com/xiaomi-mlab/mila.github.io.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Comprehensive Survey on World Models for Embodied AI

    cs.CV 2025-10 conditional novelty 6.0 of 10

    A unified three-axis taxonomy — functionality, temporal modeling, spatial representation — organizes the world-model literature for embodied AI.

  2. Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistency

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A joint video and LiDAR generation framework for driving scenes, conditioned on shared scene layouts, VLM captions, and BEV features, achieves SOTA generation and downstream perception gains on nuScenes.

  3. UNIVERSE: Unified Video Action Models for Autonomous Driving with Flexible Mask-Modulated Modality Generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A single mask-modulated DiT that co-trains future video and trajectories yields stronger autonomous-driving action generalization and 4.3× faster trajectory-only inference than dual-DiT designs.

  4. A Survey of World Models for Autonomous Driving

    cs.RO 2025-01 conditional novelty 2.0 of 10

    A survey presenting a three-branch taxonomy of world models for autonomous driving, plus benchmark tables comparing representative generation and planning methods on nuScenes, Waymo, Occ3D, and CarlaSC.

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