Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provi
FrameForge enables adaptive conditional image generation by modeling variable condition-target relationships through dynamic temporal experts and intermediate reasoning pathways in pretrained video models.
Unified conditional image generation requires a single model to preserve different aspects of a visual input while producing a target image that follows textual guidance. Pretrained video models provide a promising foundation by encoding visual transformations across time, yet existing approaches typically assign the condition and target to fixed temporal positions. We observe that condition-target relationships vary substantially across generation tasks, while video models naturally encode visual transformations at different temporal scales. This motivates us to model conditional generation through input-adaptive virtual temporal relations. We propose FrameForge, which captures two…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 14 checks on 2 different days. Its best position was #1 of 30, reached on Nov 2, 17:28 UTC. It dropped off the list after Nov 3, 07:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (14 checks) since.