3 accepted papers
Vision-Language-Action (VLA) models leverage pretrained vision-language models (VLMs) to couple perception with robotic control, offering a promising path toward general purpose embodied intelligence. However, current SOTA VLAs are primarily pretrained on multimodal tasks with limited relevance to e…
Autonomous driving progress relies on large-scale annotated datasets. In this work, we explore the potential of generative models to produce vast quantities of freely-labeled data for autonomous driving applications and present SubjectDrive, the first model proven to scale generative data production…
The field of autonomous driving increasingly demands high-quality annotated training data. In this paper we propose Panacea an innovative approach to generate panoramic and controllable videos in driving scenarios capable of yielding an unlimited numbers of diverse annotated samples pivotal for auto…