ShotPlan: Cinematic Video Generation with Learnable Planning Token

Hugging Face Daily Papers Papers
video-generation cinematic-video diffusion-model planning-tokens shot-planning artificial-intelligence

Summary

ShotPlan introduces learnable planning tokens with fractional temporal rotary position embeddings for cinematic multi-shot video generation, enabling explicit shot-level planning and achieving superior inter-shot consistency.

Current video generation models achieve impressive results in single-shot generation, yet remain limited in cinematic video generation, where coherent narratives and effective multi-shot composition require explicit shot planning. To address this challenge, we propose ShotPlan, a framework for explicit multi-shot cinematic video generation built upon a video diffusion foundation model. Our method introduces learnable planning tokens that capture shot-level transition cues and can be seamlessly integrated with the original video generation tokens to control transition timestamps. Unlike standard video generation tokens, the proposed planning tokens are equipped with Fractional Temporal Rotary Position Embedding (FRoPE), enabling shot transitions to be modeled at the frame level. Experiments demonstrate that ShotPlan significantly outperforms existing cinematic video generation methods, offering more flexible shot management and stronger inter-shot consistency.
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Paper page - ShotPlan: Cinematic Video Generation with Learnable Planning Token

Source: https://huggingface.co/papers/2607.17675 Published on Jul 20

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Submitted byhttps://huggingface.co/Pensioner

suguoon Jul 21

Abstract

Currentvideogenerationmodelsachieveimpressiveresultsinsingle-shotgeneration,yetremainlimitedincinematicvideogeneration,wherecoherentnarrativesandeffectivemulti-shotcompositionrequireexplicitshotplanning.Toaddressthischallenge,weproposeShotPlan,aframeworkforexplicitmulti-shotcinematicvideogenerationbuiltuponavideodiffusionfoundationmodel.Ourmethodintroduceslearnableplanningtokensthatcaptureshot-leveltransitioncuesandcanbeseamlesslyintegratedwiththeoriginalvideogenerationtokenstocontroltransitiontimestamps.Unlikestandardvideogenerationtokens,theproposedplanningtokensareequippedwithFractionalTemporalRotaryPositionEmbedding(FRoPE),enablingshottransitionstobemodeledattheframelevel.ExperimentsdemonstratethatShotPlansignificantlyoutperformsexistingcinematicvideogenerationmethods,offeringmoreflexibleshotmanagementandstrongerinter-shotconsistency.

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