ORV: 4D Occupancy-centric Robot Video Generation
This work addresses the need for efficient and high-quality simulation data in robot learning, offering a solution that enhances video generation for downstream tasks like gripping operations.
The paper tackles the problem of generating realistic robot simulation videos by proposing ORV, a framework that uses 4D semantic occupancy sequences for fine-grained guidance, resulting in improved control precision and generalization compared to existing methods.
Acquiring real-world robotic simulation data through teleoperation is notoriously time-consuming and labor-intensive. Recently, action-driven generative models have gained widespread adoption in robot learning and simulation, as they eliminate safety concerns and reduce maintenance efforts. However, the action sequences used in these methods often result in limited control precision and poor generalization due to their globally coarse alignment. To address these limitations, we propose ORV, an Occupancy-centric Robot Video generation framework, which utilizes 4D semantic occupancy sequences as a fine-grained representation to provide more accurate semantic and geometric guidance for video generation. By leveraging occupancy-based representations, ORV enables seamless translation of simulation data into photorealistic robot videos, while ensuring high temporal consistency and precise controllability. Furthermore, our framework supports the simultaneous generation of multi-view videos of robot gripping operations - an important capability for downstream robotic learning tasks. Extensive experimental results demonstrate that ORV consistently outperforms existing baseline methods across various datasets and sub-tasks. Demo, Code and Model: https://orangesodahub.github.io/ORV