arXiv:2606.04775v1 Announce Type: new Abstract: Text-to-video (T2V) models trained on large-scale web data can generate undesired content, motivating interventions that reduce harmful outputs without sacrificing visual quality. Activation steering offers an attractive mechanistic alternative to finetuning and prompt filtering, but existing T2V steering methods remain limited, typically applying coarse, non-anticipative interventions that can lead to oversteering and content degradation. To close this gap, we propose Latent Activation Linear-Quadratic Regulator (LA-LQR), a reduced-order optimal
The proliferation of advanced text-to-video models necessitates more sophisticated control mechanisms to mitigate undesired content and improve content generation fidelity, addressing current limitations in steering methods.
This development allows for more precise and anticipative control over AI-generated video content, reducing risks of harmful outputs and enhancing the overall quality and usability of T2V models for various applications.
The ability to finely control latent activations in video generation models via optimal control methods improves content quality and alignment with desired outcomes, moving beyond coarse interventions.
- · AI content platforms
- · Generative AI researchers
- · Video production industries
- · Content moderation developers
- · Platforms with poor content moderation
- · Coarse steering method developers
AI video generation becomes both more controllable and higher quality, enabling broader commercial adoption.
Improved content moderation at the generation stage reduces the need for extensive post-production filtering and public content review.
Enhanced control over AI-generated media may influence public trust and perception of synthetic content, potentially increasing its integration into critical applications.
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Read at arXiv cs.LG