Cut2Next: Generating Next Shot via In-Context Tuning
Effective multi-shot generation demands purposeful, film-like transitions and
strict cinematic continuity. Current methods, however, often prioritize basic
visual consistency, neglecting crucial editing patterns (e.g., shot/reverse
shot, cutaways) that drive narrative flow for compelling storytelling. This
yields outputs that may be visually coherent but lack narrative sophistication
and true cinematic integrity. To bridge this, we introduce Next Shot Generation
(NSG): synthesizing a subsequent, high-quality shot that critically conforms to
professional editing patterns while upholding rigorous cinematic continuity.
Our framework, Cut2Next, leverages a Diffusion Transformer (DiT). It employs
in-context tuning guided by a novel Hierarchical Multi-Prompting strategy. This
strategy uses Relational Prompts to define overall context and inter-shot
editing styles. Individual Prompts then specify per-shot content and
cinematographic attributes. Together, these guide Cut2Next to generate
cinematically appropriate next shots. Architectural innovations, Context-Aware
Condition Injection (CACI) and Hierarchical Attention Mask (HAM), further
integrate these diverse signals without introducing new parameters. We
construct RawCuts (large-scale) and CuratedCuts (refined) datasets, both with
hierarchical prompts, and introduce CutBench for evaluation. Experiments show
Cut2Next excels in visual consistency and text fidelity. Crucially, user
studies reveal a strong preference for Cut2Next, particularly for its adherence
to intended editing patterns and overall cinematic continuity, validating its
ability to generate high-quality, narratively expressive, and cinematically
coherent subsequent shots.
689bfef7fab6fdd2e52ac837
Jingwen He
claimed_verified
2025-08-14T13:38:51.784Z
66a49f9b2b460286b05646b8
/avatars/5123cf7c3d00b5bc47a86011afd6abe8.svg
jwhe
user
jwhejwhe
689bfef7fab6fdd2e52ac838
Hongbo Liu
689bfef7fab6fdd2e52ac839
Jiajun Li
689bfef7fab6fdd2e52ac83a
Ziqi Huang
689bfef7fab6fdd2e52ac83b
Yu Qiao
689bfef7fab6fdd2e52ac83c
Wanli Ouyang
689bfef7fab6fdd2e52ac83d
Ziwei Liu
Cut2Next: Generating Next Shot via In-Context Tuning
Effective multi-shot generation demands purposeful, film-like transitions and
strict cinematic continuity. Current methods, however, often prioritize basic
visual consistency, neglecting crucial editing patterns (e.g., shot/reverse
shot, cutaways) that drive narrative flow for compelling storytelling. This
yields outputs that may be visually coherent but lack narrative sophistication
and true cinematic integrity. To bridge this, we introduce Next Shot Generation
(NSG): synthesizing a subsequent, high-quality shot that critically conforms to
professional editing patterns while upholding rigorous cinematic continuity.
Our framework, Cut2Next, leverages a Diffusion Transformer (DiT). It employs
in-context tuning guided by a novel Hierarchical Multi-Prompting strategy. This
strategy uses Relational Prompts to define overall context and inter-shot
editing styles. Individual Prompts then specify per-shot content and
cinematographic attributes. Together, these guide Cut2Next to generate
cinematically appropriate next shots. Architectural innovations, Context-Aware
Condition Injection (CACI) and Hierarchical Attention Mask (HAM), further
integrate these diverse signals without introducing new parameters. We
construct RawCuts (large-scale) and CuratedCuts (refined) datasets, both with
hierarchical prompts, and introduce CutBench for evaluation. Experiments show
Cut2Next excels in visual consistency and text fidelity. Crucially, user
studies reveal a strong preference for Cut2Next, particularly for its adherence
to intended editing patterns and overall cinematic continuity, validating its
ability to generate high-quality, narratively expressive, and cinematically
coherent subsequent shots.
Cut2Next, a framework using a Diffusion Transformer with in-context tuning and hierarchical prompting, generates high-quality, cinematically coherent shots that adhere to professional editing patterns.
Diffusion Transformer DiT in-context tuning Hierarchical Multi-Prompting Relational Prompts Individual Prompts Context-Aware Condition Injection CACI Hierarchical Attention Mask HAM RawCuts CuratedCuts CutBench