DriveCtrl: Conditioned Sim-to-Real Driving Video Generation
DriveCtrl narrows the sim-to-real gap in driving videos using depth-conditioned generation.
Key Findings
Methodology
DriveCtrl is a depth-conditioned controllable sim-to-real video generation framework. It builds upon a pretrained video foundation model and introduces a structure-aware adapter for depth-guided generation, preserving the scene layout and motion patterns of the source simulation. The framework supports three conditioning signals: structural depth, reference-dataset style, and text prompts, ensuring the generated videos visually match the target real-world dataset.
Key Results
- DriveCtrl achieved DVRS scores of 10.63 and 10.47 on the Cityscapes and Waymo datasets, significantly outperforming baseline methods.
- In VBench evaluation, DriveCtrl excelled in background consistency, subject consistency, and motion smoothness compared to other methods.
- In downstream perception tasks, data generated by DriveCtrl improved model generalization in semantic segmentation tasks.
Significance
The introduction of DriveCtrl is significant for both academia and industry. By narrowing the sim-to-real gap, it reduces the cost of large-scale data collection for autonomous driving systems while enhancing data diversity and realism. This technical breakthrough addresses long-standing domain adaptation challenges, providing new avenues for autonomous driving system development.
Technical Contribution
DriveCtrl's technical contributions include its structure-aware adapter and the new evaluation metric DVRS. Compared to existing methods, DriveCtrl can generate visually realistic videos while maintaining structural consistency. Additionally, DVRS offers a more driving-domain-relevant evaluation standard, filling gaps in semantic and physical consistency assessment.
Novelty
DriveCtrl is the first framework to apply depth-conditioned generation to sim-to-real video translation. Unlike previous methods, DriveCtrl not only focuses on visual quality but also ensures that the generated video's scene structure and motion patterns align with the source simulation data through its structure-aware adapter.
Limitations
- In extreme weather conditions, the generated videos may not maintain high visual consistency.
- The method requires significant computational resources, which may limit its application in resource-constrained environments.
Future Work
Future research could explore the applicability of DriveCtrl in various driving scenarios, such as nighttime driving or complex urban environments. Additionally, integrating more sensor data, like LiDAR, could further enhance the realism and diversity of generated videos.
AI Executive Summary
DriveCtrl is an innovative depth-conditioned controllable video generation framework designed to bridge the gap between simulated and real driving videos. Traditional video generation methods struggle to maintain scene structure and dynamic consistency, whereas DriveCtrl introduces a structure-aware adapter to achieve depth-guided generation, ensuring the generated videos visually match the target real-world dataset.
In experiments, DriveCtrl significantly outperformed existing methods on the Cityscapes and Waymo datasets, achieving DVRS scores of 10.63 and 10.47, respectively. It also excelled in VBench evaluations, showing superior performance in background consistency, subject consistency, and motion smoothness compared to other methods.
The technological breakthrough of DriveCtrl offers new insights into data collection and training for autonomous driving systems. By generating high-quality simulated videos, researchers can obtain more diverse and realistic data without increasing costs. This technology will greatly accelerate the development of autonomous driving technology and provide rich directions for future research.
Deep Analysis
Background
The development of autonomous driving technology relies on large-scale labeled driving video data. However, acquiring real-world data is costly and time-consuming, especially for capturing rare but safety-critical scenarios. Synthetic data offers a scalable alternative, but the domain gap between synthetic and real data limits its utility in practical applications. Recent advances in video generation have made significant progress, yet challenges remain in maintaining scene structure and dynamic consistency.
Core Problem
The domain gap between simulated and real driving videos is a core issue in autonomous driving data generation. Existing methods struggle to balance visual quality and structural consistency, rendering the generated videos ineffective for downstream perception tasks. Solving this problem is crucial for reducing data collection costs and improving the robustness of autonomous driving systems.
Innovation
DriveCtrl's core innovations include its structure-aware adapter and depth-conditioned generation technology. By introducing depth-guided generation mechanisms, DriveCtrl can generate visually realistic videos while preserving scene structure and motion patterns. Additionally, DriveCtrl supports multiple conditioning signal inputs, providing flexible generation control capabilities.
Methodology
- �� Structure-aware Adapter: Introduced on top of a pretrained video foundation model to ensure depth-guided generation.
- �� Data Generation Pipeline: Converts simulator videos into driving videos matching the target real-world dataset style.
- �� DVRS Evaluation Metric: Assesses generated videos from dimensions such as plausibility, consistency, and visual realism.
Experiments
Experiments were conducted using the Cityscapes and Waymo datasets. By using depth videos from the SHIFT dataset as structural input, videos matching the target dataset style were generated. Evaluation metrics included DVRS and VBench to comprehensively assess the visual quality and structural consistency of the generated videos.
Results
DriveCtrl achieved DVRS scores of 10.63 and 10.47 on the Cityscapes and Waymo datasets, significantly outperforming baseline methods. In VBench evaluation, DriveCtrl excelled in background consistency, subject consistency, and motion smoothness compared to other methods. Additionally, the generated data performed well in downstream semantic segmentation tasks.
Applications
DriveCtrl can be used for data generation and training in autonomous driving systems, especially in scenarios requiring large-scale labeled data. By generating high-quality simulated videos, researchers can obtain more diverse and realistic data without increasing costs.
Limitations & Outlook
DriveCtrl may not perform as expected under extreme weather conditions. Additionally, its demand for computational resources may limit its application in resource-constrained environments. Future research could explore integrating more sensor data to enhance the realism of generated videos.
Plain Language Accessible to non-experts
Imagine you're playing a driving simulation game. The graphics look realistic, but they don't quite match what you see in real life. That's because the game scenes and objects, while lifelike, lack the detail and dynamics of the real world. DriveCtrl acts like a super filter that makes these simulated scenes more realistic, making you feel like you're actually driving. It uses a technique called 'depth-conditioned generation' to ensure that every object and scene in the video follows real-world physics and visual effects.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool racing game. The graphics look pretty real, but they still seem a bit different from what you see on the road. DriveCtrl is like adding a super filter to the game, making the graphics look just like real life. It uses something called 'depth-conditioned generation' to make sure every scene and action looks real. So, when you're playing, it feels like you're really there! Isn't that awesome?
Glossary
Sim-to-Real
Refers to the transition from simulated environments to real environments, often used to reduce the gap between synthetic and real data.
In DriveCtrl, Sim-to-Real refers to converting simulated driving videos into real-style videos.
Depth Conditioned
Utilizes depth information as a generation condition to ensure structural consistency of generated content.
DriveCtrl uses depth-conditioned generation to ensure the scene structure of generated videos aligns with the simulation source.
DVRS
Driving Video Realism Score, used to evaluate the visual realism and structural consistency of generated videos.
DVRS is used in DriveCtrl's evaluation to measure the quality of generated videos.
Structure-aware Adapter
A module for video generation that ensures structural consistency of generated content.
DriveCtrl achieves depth-guided generation through its structure-aware adapter.
VBench
A benchmark for evaluating video generation quality across multiple dimensions.
In DriveCtrl's experiments, VBench is used to assess the visual quality of generated videos.
Open Questions Unanswered questions from this research
- 1 How to maintain high-quality video generation under extreme weather conditions? Existing methods perform poorly in these conditions, requiring further research.
- 2 How to reduce DriveCtrl's computational resource demands for application in resource-constrained environments?
Applications
Immediate Applications
Autonomous Driving Data Generation
DriveCtrl can be used to generate high-quality simulated driving videos, reducing data collection costs and enhancing data diversity.
Long-term Vision
Intelligent Transportation Systems
By generating more realistic traffic scene data, DriveCtrl can facilitate the development and testing of intelligent transportation systems.
Abstract
Large-scale labelled driving video data is essential for training autonomous driving systems. Although simulation offers scalable and fully annotated data, the domain gap between synthetic and real-world driving videos significantly limits its utility for downstream deployment. Existing video generation methods are not well-suited for this task, as they fail to simultaneously preserve scene structure, object dynamics, temporal consistency, and visual realism, all of which are critical for maintaining annotation validity in generated data. In this paper, we present DriveCtrl, a depth-conditioned controllable sim-to-real video generation framework for realistic driving video synthesis. Built upon a pretrained video foundation model, DriveCtrl introduces a structure-aware adapter that enables depth-guided generation while preserving the scene layout and motion patterns of the source simulation, producing temporally coherent driving videos that remain aligned with the original simulated sequences. We further introduce a scalable data generation pipeline that transforms simulator videos into realistic driving footage matching the visual style of a target real-world dataset. The pipeline supports three conditioning signals: structural depth, reference-dataset style, and text prompts, while preserving frame-level annotations for downstream perception tasks. To better assess this task, we propose a driving-domain-specific knowledge-informed evaluation metric called Driving Video Realism Score (DVRS) that assesses the realism of generated videos. Experiments demonstrate that DriveCtrl consistently outperforms the base model and competing alternatives in realism, temporal quality, and perception task performance, substantially narrowing the sim-to-real gap for driving video generation.