arXiv:2603.07615v3 Announce Type: replace Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixels, latents, or tokens) remain external to the model and cannot directly exploit this knowledge for compact storage or reuse. In this work, we introduce a new visual representation framework that encodes a signal as a function, which is parametrized by low-rank adaptations attached to a frozen visual generative model. Such implicit representations of visual signals, \textit{e.g.}, an 81-frame video, can
The proliferation of visual generative AI models creates a pressing need for more efficient and knowledge-aware data representation, which this research directly addresses.
This development proposes a fundamentally new way to store and reuse visual data by embedding it within AI models, potentially leading to significant advancements in data compression and AI efficiency.
Visual data representation could shift from external explicit forms (pixels, latents) to implicit functions embedded within generative AI models, fundamentally altering how visual information is handled.
- · AI developers
- · Generative AI platforms
- · Data storage providers
- · Graphics and media industries
- · Traditional visual compression algorithms
Reduced data storage and transmission costs for visual content.
Faster AI training and inference through more efficient data handling and reuse.
The emergence of new AI applications and services built on these highly compressed, generative model-native visual representations.
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Read at arXiv cs.LG