ORV: 4D Occupancy-centric Robot Video Generation
Authors: name: Xiuyu Yang, name: Bohan Li, name: Shaocong Xu, name: Nan Wang, name: Chongjie Ye, name: Zhaoxi Chen, name: Minghan Qin, name: Yikang Ding, name: Xin Jin, name: Hang Zhao, name: Hao Zhao

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