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CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
multi-shot video generation
Abstract:

Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transition capabilities remain rudimentary and unstable, largely confining generated videos to single-shot sequences. In this work, we introduce CineTrans, a novel framework for generating coherent multi-shot videos with cinematic, film-style transitions. To facilitate insights into the film editing style, we construct a multi-shot video-text dataset Cine250K with detailed shot annotations. Furthermore, our analysis of existing video diffusion models uncovers a correspondence between attention maps in the diffusion model and shot boundaries, which we leverage to design a mask-based control mechanism that enables transitions at arbitrary positions and transfers effectively in a training-free setting. After fine-tuning on our dataset with the mask mechanism, CineTrans produces cinematic multi-shot sequences while adhering to the film editing style, avoiding unstable transitions or naive concatenations. Finally, we propose specialized evaluation metrics for transition control, temporal consistency and overall quality, and demonstrate through extensive experiments that CineTrans significantly outperforms existing baselines across all criteria.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces CineTrans, a framework for generating multi-shot videos with cinematic transitions, alongside the Cine250K dataset with shot annotations and a mask-based control mechanism. It resides in the 'Transition-Aware Video Synthesis' leaf, which contains only four papers total, including this one. This leaf sits within the broader 'Cinematic Transition and Editing Control' branch, indicating a relatively sparse research direction focused specifically on modeling shot boundaries and transition quality rather than general multi-shot generation or narrative structure.

The taxonomy reveals that neighboring leaves address related but distinct challenges: 'Editing Pattern and Cinematographic Language Learning' (five papers) focuses on learning professional editing conventions from film data, while 'Narrative-Driven Multi-Shot Synthesis' (five papers) emphasizes story decomposition and shot-by-shot generation. The 'Attention-Based Multi-Shot Control' leaf (three papers) explores attention mechanisms for cross-shot consistency. CineTrans bridges transition modeling with editing pattern learning by constructing an annotated dataset and leveraging attention map analysis, positioning it at the intersection of explicit transition control and data-driven cinematic language understanding.

Among 30 candidates examined, the Cine250K dataset contribution shows no clear refutation across 10 candidates, suggesting novelty in providing detailed shot annotations for multi-shot video generation. The mask-based control mechanism examined 10 candidates with 4 appearing to provide overlapping prior work, indicating more substantial existing research on attention-based or mask-based transition control. The CineTrans framework itself examined 10 candidates with 1 refutable match, suggesting the integrated system approach may offer incremental novelty over existing transition-aware methods within this limited search scope.

Based on the top-30 semantic matches examined, the work appears to contribute primarily through its dataset and integrated framework rather than fundamentally novel transition mechanisms. The sparse taxonomy leaf (four papers) suggests transition-aware synthesis remains an emerging direction, though the refutation statistics indicate that specific technical components overlap with prior attention-based control methods. The analysis does not cover exhaustive literature beyond these candidates, leaving open questions about broader field coverage.

Taxonomy

Core-task Taxonomy Papers
40
3
Claimed Contributions
30
Contribution Candidate Papers Compared
5
Refutable Paper

Research Landscape Overview

Core task: generating multi-shot videos with cinematic transitions. This emerging field addresses the challenge of synthesizing coherent video sequences that span multiple shots with professional-quality transitions and editing conventions. The taxonomy reveals several complementary research directions: Multi-Shot Video Generation Frameworks develop end-to-end systems for producing complete video narratives; Cinematic Transition and Editing Control focuses on modeling shot boundaries, cuts, and temporal coherence across scenes; Camera and Shot Control methods enable precise manipulation of cinematographic parameters like framing and movement; Datasets and Benchmarks establish evaluation standards for cinematic quality; Domain-Specific and Multimodal approaches tailor generation to particular content types or input modalities; and Supporting Methods provide foundational techniques for video synthesis. Works like VideoGen of Thought[1] and CineVerse[2] exemplify comprehensive frameworks, while ShotAdapter[3] and Shot Sequence Ordering[4] tackle specific aspects of shot-level control and sequencing. Recent efforts reveal a tension between holistic narrative generation and fine-grained control over individual shots and transitions. Some approaches prioritize seamless temporal extension and transition smoothness, as seen in StreamingT2V[5] and related autoregressive methods, while others emphasize explicit modeling of cinematic grammar through shot planning and editing rules. CineTrans[0] sits within the transition-aware synthesis cluster, focusing specifically on learning and generating natural transitions between shots—a capability that distinguishes it from broader multi-shot frameworks like ShotDirector[15] or MAVIN[11], which may emphasize shot composition or narrative structure over transition quality. This positioning reflects a growing recognition that professional video synthesis requires not just generating individual shots but also mastering the subtle temporal and visual continuity that defines cinematic storytelling, an area where explicit transition modeling offers advantages over purely autoregressive or frame-by-frame generation strategies.

Claimed Contributions

Cine250K multi-shot video dataset with shot annotations

The authors develop a dataset of 250K video-text pairs featuring frame-level shot labels and hierarchical captions. This dataset captures film editing style and provides prior knowledge for generating cinematic multi-shot sequences.

10 retrieved papers
Mask-based control mechanism for cinematic transitions

The authors introduce a block-diagonal mask mechanism applied to attention layers in diffusion models. This mechanism enforces strong intra-shot correlations and weak inter-shot correlations, enabling precise frame-level control of cinematic transitions even without fine-tuning.

10 retrieved papers
Can Refute
CineTrans framework for multi-shot video generation

The authors propose CineTrans, a framework that combines the mask mechanism with fine-tuning on Cine250K to generate multi-shot videos with cinematic transitions. The framework produces videos that adhere to film editing conventions while maintaining temporal consistency.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Cine250K multi-shot video dataset with shot annotations

The authors develop a dataset of 250K video-text pairs featuring frame-level shot labels and hierarchical captions. This dataset captures film editing style and provides prior knowledge for generating cinematic multi-shot sequences.

Contribution

Mask-based control mechanism for cinematic transitions

The authors introduce a block-diagonal mask mechanism applied to attention layers in diffusion models. This mechanism enforces strong intra-shot correlations and weak inter-shot correlations, enabling precise frame-level control of cinematic transitions even without fine-tuning.

Contribution

CineTrans framework for multi-shot video generation

The authors propose CineTrans, a framework that combines the mask mechanism with fine-tuning on Cine250K to generate multi-shot videos with cinematic transitions. The framework produces videos that adhere to film editing conventions while maintaining temporal consistency.

Table of Contents