arXiv:2607.00310v1 Announce Type: cross Abstract: Foundation video diffusion models are increasingly viewed as world simulators for embodied agents, yet their pretraining on internet-scale generic video leaves them poorly aligned with real-world deployment domains. We study parameter-efficient adaptation of a pretrained foundation video world model to retail scenes: when synchronized egocentric and exocentric video of the same activity are available, which viewpoint of training data produces the strongest adapted model? We introduce RetailSMV (Retail Synchronized Multi-View), a corpus of 32,10
The proliferation of advanced video foundation models and the specific need for their adaptation to real-world, industry-specific tasks like retail surveillance are driving this research.
This research addresses the critical challenge of making powerful AI world models practically applicable by demonstrating parameter-efficient adaptation to specific domains, which is key for commercial deployment.
The ability to efficiently adapt generic foundation video models to specific retail environments means AI can more accurately and effectively analyze real-world events, leading to new applications in security, inventory, and customer experience.
- · Retailers
- · AI Vision System Providers
- · Smart City Developers
- · Generic AI model developers
More accurate and deployable AI video analysis systems in retail environments.
Improved operational efficiency and security within retail leading to cost savings and new consumer insights.
The development of highly specialized, context-aware AI agents for complex physical world interactions, impacting various industries beyond retail.
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Read at arXiv cs.AI