arXiv:2606.24196v1 Announce Type: new Abstract: Modern AIGC pipelines deliver high-fidelity images and videos but presuppose a well-formed creation instruction, while end users rarely articulate visual details, leaving generators misaligned with user demand. We study personalized content generation, which turns a user's interaction history into an executable instruction for downstream synthesis, and identify two obstacles: behavior must be encoded in a form legible to language reasoning, and the model must acquire instruction-writing skill absent from both pretraining and behavior data. We pro
The proliferation of AIGC pipelines highlights the gap between user intent and model output, necessitating immediate solutions for personalized generation.
This research addresses a core limitation in generative AI, moving it from 'creation instruction' to 'user behavior,' which is crucial for mass adoption and effective human-AI interaction.
Generative AI systems will become more adept at understanding and translating implicit user behavior into explicit, high-fidelity content generation instructions, moving beyond explicit prompting.
- · AI platform developers
- · Creative professionals
- · Content creators
- · End users of generative AI
- · Generative AI models with poor personalization capabilities
- · Manual prompt engineers
- · Platforms that cannot adapt to user behavior
Personalized content generation becomes more fluid and intuitive, reducing the friction in creating digital assets.
This deepens user engagement with AI platforms as they can anticipate and fulfill creative desires without explicit input.
It could fundamentally alter how digital content is created and consumed, leading to highly customized and context-aware media experiences across all platforms.
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Read at arXiv cs.AI