arXiv:2606.29095v1 Announce Type: cross Abstract: Diffusion-based video relighting enables controllable relighting from a single input video, but modern video diffusion backbones are trained on short clips and applied to long-horizon videos through chunked sliding-window inference, often causing temporal discontinuities at chunk boundaries. We address this by reframing long-horizon relighting as \emph{temporally conditioned latent domain translation}. Our framework enforces cross-chunk continuity by propagating target-domain latents across boundaries and makes this behavior learnable using \em
The proliferation of diffusion models in video generation and editing has highlighted the current limitations in handling long-form content consistently, prompting research into novel architectural solutions.
This development addresses a key technical hurdle in applying generative AI to longer video sequences, enabling more stable and realistic artistic and practical applications.
The ability to consistently relight long-horizon videos without temporal discontinuities improves the quality and applicability of AI-driven video content creation and editing workflows.
- · Video production studios
- · Metaverse developers
- · Generative AI companies
- · Content creators
- · Manual video editors doing relighting
- · Companies relying on short-form video AI tools
Improved realism and control for AI-generated video content, especially for longer narratives.
Expansion of AI's role in film, advertising, and virtual reality with more sophisticated visual effects and environmental adaptation.
The democratization of complex visual effects, making high-fidelity video production accessible to smaller teams and individual creators.
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Read at arXiv cs.LG