CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Authors: Xiaoxue Wu
Bingjie Gao
Yu Qiao
Yaohui Wang
Xinyuan Chen
Venue: ICLR 2026 Poster (2026)
Keywords: multi-shot video generation
Primary area: generative models

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.

Published as a conference paper at ICLR 2026
CINETRANS: LEARNING TO GENERATE VIDEOS WITH CINEMATIC TRANSITIONS VIA MASKED DIFFUSION MODELS
Xiaoxue Wu
Bingjic Gao
Qiao²
Yaohui Wang"1
Xinyuan Chen
Fudan University, Shanghai Artificial Intelligence Laboratory, Shanghai Jiao Tong University
Shot
Shot
The video captures tranquil lakeside et sunset and the sky is deep oranges, purples, and saft pinks.
Shot 1: 40s, Shot 2: (46.8o)
Shot
Shot Shott 1: [0s,2.75s): Shot 2: (2.75s.5s)
A woman with striking emerald-green eyes lifts weathered leather journal to her lips.
aged runs rising
[0s.2.75a] (2.75s.8
The video captures high-angle shot of a city at night.
Shot [0s.2.5o]: Shot [2.56.5e] Shot (5s.8s)
Figure I:
Multi-shot videos generated by CineTraas, which enables cinematic transitions aligning with film editing. The corresponding mask is constructed based the timestamps of the shots, thereby controlling cinematic transitions Project page: https::/ukkowwsth github. io/ CineTrans
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 anno- tations. Furthermore, our analysis of existing video diffusion models uncovers correspondence between attention maps the diffusion model and shot bound- aries, which leverage to design mask-based control mechanism that enables transitions at arbitrary positions and transfers effectively in a training-free setting. After fine-tuning our dataset with the mask mechanism, CineTrans produces cinematic multi-shot sequences while adhering to the film editing style, avoid- ing unstable transitions naive concatenations. Finally, we propose specialized evaluation metrics for transition control, temporal consistency and overall qual- ity, and demonstrate through extensive experiments that CineTrans significantly outperforms existing baselines across all criteria.
"Corresponding authors.

---

Published as a conference paper at ICLR 2026
1 INTRODUCTION
Endowed with extensive pre-training and sophisticated architectures, diffusion models (Blattmann et al., 2023; Ho et al., 2020; Rombach et al., 2022; Song et al., b) have demonstrated promising capabilities in generating videos with high visual quality and strong consisteney comparable to real videos (Wan et al., 2025; Kong et al., 2024; Yang et al., 2024; Li et al., 2024). Particularly in the domain of text-to-video generation (T2V) (Wang et al., 2024b; Yang et al., 2024; Li et al., 2023; Cho et al., 2024), the breakthrough has attracted considerable attention, highlighting the potential of diffusion models in mastering video creation (Wang et al., 2024b; Xu et al., 2024; Chen et al., 2023a; 2024a). However, generating multi-shot videos with cinematic editing style and movie-like transitions from brief user input continues to be significant challenge.
Existing work on long video generation can be broadly categorized into two aspects. First, larger models trained on massive datasets advance the capability of video interpretation and generation, enhancing the visual fidelity and maximum video length. (Kong et al., 2024; Wan et al., 2025; Yang et al., 2024). Second, techniques such as conditioning improve consistency across concatenated samples, enabling longer outputs through clip stitching (Zheng et al., 2024; He et al., 2023; Xie et al., 2024; Zhao et al., 2024). While both approaches partially support multi-shot sequence gener- ation, they still exhibit notable limitations. Large-scale models primarily focus on single-shot videos due to the scarcity of shot transitions in their training datasets (Wang ct al., 2023b; Ju et al., 2025; Wang ct al., 2023a). Cinematic transitions are not guaranteed, let alone at precisely controlled posi- tions. Moreover, the high computational cost and extended training time undermine the efficiency of this approach. Meanwhile, generating individual shots separately and concatenating them requires substantial manual intervention, and ignores prior knowledge from cinematic multi-shot dataset, re- sulting in cuts that often misalign with real-world editing styles. Additionally, many recent works often target narrow contexts, such as facial consistency (Zheng et al., 2024) or specific animated series (Dalal et al., 2025), which constrains their applicability to general video generation.
While the aforementioned methods achieve certain multi-shot effects, very few explicitly focus on cinematic transitions within diffusion models. In this work, we propose Cine framework that produces multi-shot videos with cinematic transitions as shown in Figure 1. To support this, we develop refined data processing pipeline that processes raw footage into dataset of 250K video-text pairs. Our split-stitch procedure groups semantically related clips and removes gradual transitions, and we employ transition-aware models to annotate hierarchical captions. The resulting Cine250K provides frame-level shot labels and temporally- -ense captions, preserving film editing style and making it well-suited for multi-shor video generation, while proving effective in enabling more natural shot transitions and stronger inter-shot consistency.
To analyze how brafus models handle cinematic multi-shot sequences, we dive deep into the attention patterns, examining attention maps across the temporal dimension. We find that the attention maps have strong impact on the intra-shot and inter-shot frames. This insight clarifies the underlying mechanism of shot transitions in diffusion models. Building on this, we introduce mask mechanism featuring strong correlations within shots and weak correlations between shots, achieving controlled cinematic transitions and enabling zero-shot multi-shot generation.
As shown in Figure our proposed CineTrans framework functions through two key aspects. First, through an analysis of the attention maps, we prove that the mask mechanism aligns with the dif- fusion model's 's inherent understanding of cinematic multi-shot sequences. The application of mask enables strong intra-shot frame correlations in attention module, facilitating precise frame-level cin- ematic transitions, which remains effective even in a training-free setting. Second, the constructed Cine250K encapsulates prior knowledge of film editing. Fine-tuning this dataset equips Cine- Trans with the ability to generate cinematic transitions that conform to this style, rather than simply concatenating semantically similar clips. Consequently, CineTrane is able to producing cinematic multi-shot videos aligned with film-editing conventions.
We evaluate the model series of prompts with specified transitions and conduct comprehen- sive analysis using multiple metrics from different perspectives. Experimental results demonstrate that CineTrans achieves finer shot transition control and stronger consistency compared to other multi-shot generation methods, without compromising overall quality.
Our contributions are summarized follows:
2

---

Published as a conference paper at ICLR 2026
Long Video Generation
Observing Multi-shot case
Cinematic Multi-shot Video Generation
The vidco condicts two shots. Focused mon logs dawn, sun glowing determned frame.
The video
Focused man jogo dawe, gizwing on debermindd frome.
0000000000
Inter
apply
D00 000001 1
Mask
Noise
Noisn
Primarly single lacking controlability
Strang introwshot comeeations wesk inter-shot correlations.
Attention
Detaset
Cinemaaii if wehot vsden
Film Editing Multi shot Video
Construct Multi-shot Video Dataset
begins
Coption
Shar1: frome --fomedd::: Shat2: fromeb3 3-mram-1000
sunddd rolling Hills and mountaine oon Inamstioon to closer scricd siow same Irde The video corcautes
Shot Label
Figure 2: Overview of Cine Existing video generation models focus primarily on single- shot video. The multi-shot video generation cases often follow several failures, remaining unstable and uncontrollable. Observations of these multi-shot cases reveal a structured pattern in attention layers. Based on this insight, we introduce a mask mechanism and fine-tune the model with our constructed dataset Cine250K, resulting in significantly improved performance.
We developed dataset of 250K video-text pairs, complete with frame-level shot labels and hierarchical annotations, which facilitates video diffusion models for generating cinematic transitions and consistency between shots.
We analyze attention maps in diffusion models for multi-shot video generation and observe a strong connection between attention probabilities and shot transitions. Building on this insight, we introduce mask mechanism that enables cinematic transitions within diffusion models, leading to the CineTrans framework, which is effective in training-free setting.
We propose a series of comprehensive metrics tailored for cinematic multi-shot video gen- eration and evaluate Cine'Trans, demonstrating its ability control cinematic transitions, enhance temporal consistency, and preserve overall quality.
2 RELATED WORK
bassed Video Generation. Diffusion-based approaches, built on the iterative denoising framework of Latent Diffusion Model (Rombach al., 2022), utilize scaled-up model (Kong al., 2024; Wan et al., 2025; Yang al., 2024; Blattmann et al., 2023; Chen et al., 2023a; 2024a; He et al., 2022; Lin et al., 2024; Polyak et al., 2024) and large video datasets (Miech et al., 2019; Bain et al. 2021; Zellers et al., 2021; Xue et al., 2022; Wang et al., 2023a;b; Chen al., 2024b; Xiong ct al., 2025) to generate high-quee gugh-qualtty prompt-adherent vidcos with extended durations. Owing to strong semantic capacity, certain pretrained models (Kong et al., 2024; Wan et al., 2025) can prelim- inarily generate multi-shot videos when provided with transition-speccfied prompts, but require vast computational resources, long training, and yield imprecise transitions. In contrast, our work offers frame-level stable cinematic transitions, seamlessly applied to the diffusion-based framework.
Multi-Shot Video Generation. Recent work has explored multi-shot video generation, which can be categorized into two main approaches. The first generates each shot separately and then con- catenates them, focusing on consistency between the generated shots. Animate-a-Story (He al., 2023) uses motion structure retrieval for plot-aligned clips guided by text prompts. SEINE (Chen al., 2023b) proposes a mask-based diffusion model to generate a smooth transition between shots. Vlogger (Zhuang et al., 2024) attempts to utilize the language model to generate prompts for dif- ferent shots of a video. DreamFactory (Xie et al., 2024) employs an LLM-based framework with multi-agent collaboration and keyframe iteration design. MovieDreamer (Zhao et al., 2024) adopts a hierarchical autoregressive architecture for global coherence, and VGoT (Zheng et al., 2024) uses keyframes and identity-preserving preserving embeddings to enforce temporal and character consistency. While these consistency-drris drivvnn approaches are effective to some extent, they do not leverage real multi- shot video datasets and tend to overlook complex relationships between shots, including variations
3

---

Published as a conference paper at ICLR 2026
Splitting
Stitching
Siftch as a video
Good aesthetic top 40%, duration between &s and 15s
Selection
66M
Shot count between and
250K
Captioning
* LLaVA-Video
* LLaVA-NeKT
Removing gradual changes
General caation:
Shot i: Low-angle view hightighting front Line
The video showcases red sintage Ferrart race car.. The camera ungle shifts to provide different perspectives of the car...
and Ferrori amblem clearly.
Shot ++ 1: Top-down close-up capturing sleek curves of the red car.
Shot
ShSht
Figure 3: Dataset curation pipeline. The raw video is split into several clips and then selectively stitched based on semantic features. A selection process then chooses high-quality multi-shot videos. After initial assembly, gradual changes removed. Finally, language model is used to annotate each video with a general caption and each shot with its shot caption, yielding temporally dense annotations.
in camera angles and scenes, which are crucial in human-edited videos. The second approach mod- ifies model architectures to generate multi-shot content directly. Test-Time training (Dalal et al,, 2025) adds a layer for long multi-shot generation but is tied to a specific animated series, limiting generalization. Mas (Qi al., 2025) applies shot-wise semantic masking yet focuses on text injection and assumes fixed shot durations. ShotAdapter (Kara et al., 2025) introduces transition tokens but exhibits low consistency. LCT (Guo al., 2025) enhances multi-shot understanding with specialized positional encodings but demands large-scale training. In contrast, our method generates cinematic multi-shot videos a single pass with flexible frame-level control, achieving strong per- formance even in a training-free setting and demonstrating generalizability across diverse scenarios.
Temporal-Controlled Video Generation. Several recent works in video generation focus on con- trolling the temporal dimension, enabling the synthesis of longer videos with more dynamic and fine-grained temporal control Cai et al. (2025); Li et al. (2025); Ouyang et al. (2024). VSTAR Li et al. (2025) draws inspiration from the band-matrix-like structure observed in the attention maps of real videos, and introduces a Temporal Attention Regularization strategy to enhance temporal dynamics and motion continuity during generation. Our targeted task, multi-shot video generation, can be regarded as a variant of temporally controlled video generation. It shifts the temporal con- trol from frame-level semantic evolution shot-level semantic transitions, which better aligns with real-world video editing practices and the narrative flow of long-form videos. Fundamentally, our proposed mask mechanism, like other temporally controlled generation methods, manipulates the attention distribution among visual tokens within the diffusion process to achieve more coherent and controllable video synthesis.
Masked Attention Mechanism. Masked attention has been widely used to regulate contextual dependencies and enable controllable generation. It has been applied in representation learning Bao et al. (2022); Tong et al. (2022), generative modeling Chang et al. (2022); Yu et al. (2023); Luo et al. (2023), and fine-grained spatial or compositional control in diffusion models Endo (2024); Wang et al. (2024a). Our work follows this line but employs attention masking for shot-level temporal control video diffusion, ensuring coherence across shots while preserving intra-shot consistency.
DATASET
video with cinematic transitions integrates multiple clips while preserving consistency. To capture film editing prior knowledge for multi-shot sequences, we introduce Cine250K, dataset for cine- matic video generation. As shown in Figure 3, starting from 633K richly edited videos from Vimeo", design a multi-stage preprocessing pipeline to construct the annotated multi-shot dataset.
First, the transition points are identified by Pyscenedetect (Castellano, 2024), resulting in fragmented segments. Adjacent segments with high similarity, measured by ImageBind (Girdhar et al., 2023) features, are then stitched together according to predefined rules to assemble an initial collection of 16M clips containing shot transitions. We filter this collection by aesthetic score, duration, and
hhpss//vimeo.com
4

---

Published as a conference paper at ICLR 2026
Shot
Shot 2
Fredefined segmentation
Denoising
Generated multi- shot video
Shot
Shot
D00
Mask
600
M
Leart Lownd Lever Lave1ELavett Lave11 Loverts
Attertion Map
Astention Probability
(a) A case of generated multi-shot video within diffusion model.
Construct
Allention module
(b) The architecture of the mask mechanism.
Figure 4: We observe that in multi-shot scenarios the attention maps form a block-diage boock-digonnal pattern, i.c. certain layers exhibit higher intra-shot than inter-shot frame correlations, so we design cor- responding masking mechanism. Using predefined transition points, the mask is applied to those layers of the diffusion model guide cinematic multi-shot video generation.
shot count to form the preliminary video dataset. Subsequently, since shot transitions can be cat- egorized into instantaneous hard cuts and gradual changes that blur segment boundaries, we apply TransNetV2 (Soucek & Lokoc, 2024) to detect and remove all gradual transition frames which do not clearly belong to any single shot. This yields unambiguous segments with precise shot labels, including exact start and end frame indices. In the final step, each video receives both general cap- tion produced by LLaVA- LLLVVV-Veeo--7 Video-7b-Qwes of cinematic transitions, and separate shot captions from LLaVA-NeXT (Liu et al., 2024).
(Zhang et al., 2024) for temporally-dene
descriptions
Following the pipeline, Cine250K offers high-aesthetic videos, precise shot labels, and hierarchical captions, thereby supplying rich prior knowledge for cinematic multi-shot video generation and facilitating the production videos with authentic film editing style. The details and comparison with previous video datasets presented in Appendix A and B.
4 METHODOLOGY
In this section, we introduce our insight and method for the proposed CineTraas. We first provide the necessary preliminaries in Section 4.1 to facilitate the following discussions. Section 4.2 presents our observations the attention maps of diffusion models, highlighting the differences between intra-shot and inter-shot correlations. Based this observation, introduce block-diagonal mask mechanism detailedly in Section 4.3 for cinematic multi-shot video generation. In Section 4.4, discuss the implementation of inference. An overview of the methodology is shown in Figure 4.
4.1 PRELIMINARY
Diffusion models (Ho et al., 2020; Song et al., a) are a class of generative models learning to reverse a diffusion process, which learn to corrupt data via predefined noisy Gaussian process then invert that corruption through trained neural network. Video diffusion models simply apply this same forward-reverse framework across temporal frames, yielding coherent video sequences ff,,f..... f2. fN ), where each element f, represents video frame. A key component in these models integrating the attention mechanism (Vaswani et al., 2017), which allows latent variables to focus on each other's relevant information and can be formalized as:
QK ddx
Attention Dnrention(Q.EEE K. V) softmax
V
where Q, K, are the query, key, and value matrices, and dy is the key dimensionality.
Cinematic multi-shot video generation task also follows the framework mentioned above, aiming to generate videos with cinematic transitions that are both aesthetically pleasing and compliant with the text prompts. Additionally, with the introduction of multi-shot, the core challenges include:
5

---

Published as a conference paper at ICLR 2026
Generation of cinematic transitions. The generated sequence F = (f1, f2. f2...., fx can be divided into M sub-intervals, where each Fm = {frm fhm+t1...... fim---1) contains frames from index im to 8m+11 1, for
........ M,, M is the specified shot count, and 1 = 1, iM++1 = N + 1. Within sub-intervals, frames maintain visual continuity, while noticeable changes the boundaries create cinematic transitions.
Consistency within and across shots. Within shot, visual consistency is crucial for continuity. In contrast, inter-shot consistency emphasizes high-level semantic similarity over low-level details, i.e., maintaining consistency across shots despite substantial com- positional differences. This means that its evaluation not confined to specific attributes, such as composition facial features, but is guided by film-editing conventions, ensuring applicability to general scenarios.
4.2 FRAME CORRELATION IN ATTENTION MODULE
Recently, owing the increased model size, dataset scale, and computational resources, some dif- fusion models (Kong et al., 2024; Wan et al., 2025) have demonstrated preliminary capabilities in generating multi-shot videos. However, how diffusion models internally model transitions within a video remains unclear and warrants further exploration. We hypothesize that the correlation be- tween adjacent frames at transition points is significantly different from that at non-transition points. The transition point involves substantial shift, while non non-transition point requires continuity to maintain visual coherence, making them inherently divergent.
In video diffusion models, the denoising process captures temporal correlations through the attention module. Specifically, the video latent representation is flattened into a sequence of tokens. Tokens from different frames participate in the calculation of the attention maps in specific layers, enabling the pretrained model to generate continuous, high-quality video segments. As a result, attention maps are valuable tool for analyzing frame correlations.
We explore and visualize frame-wise attention maps the context of the multi-shot video gener- ation cases. As shown in Figure 4a, it demonstrates strong correlations for intra-shot frames and weak correlations for inter-shot frames. More specifically, the attention probability matrix exhibits block-diagonal structure, with each block corresponding quantitative differences in attention probabilities across various layers, highlighting the variations between intra-shot and inter-shot correlations. To quantify this observation, compute the average ratio of mean intra-shot to inter-shot attention probabilities (26.68) and assess its correspondence with the ground-truth shot boundaries via Pearson correlation, yielding r=0.71 (p<0..1). This sug- gests the potential of leveraging the attention maps to guide the generation of multi-shot videos.
shot. Figure 4a also illustrates the
4.3 MASK MECHANISM
Building on our observation, we introduce a mask mechanism, a simple strategy that operates on the attention probability in text-to-video diffusion models. Specifically, we construct an attention mask M for the visual tokens the attention module at specific layers as follows:
if i,j E same shot
Mij = -o0 if i.j & same shot
(2)
The mask matrix is subsequently added the attention score in Eq. 1, effectively weakening the correlations across different shots:
QKT VTg
Attention( Q, V) softmax
- V.
(3)
As a result, the final attention probabilities form block-diagonal matrices, with cinematic transi- tions occurring at predefined positions. As shown in Figure 4b, transition positions are specified in advance, and the mask matrix is constructed accordingly, enabling precise control over the pro- cess. With the blck-diagonnl mask mechanism, diffusion models can generate cinematic multi-shot videos with fine mne-gnaieed control.
The mask mechanism functions through two aspects. First, it aligns with the phenomenon observed in Section 4.2, conforming to diffusion models' inherent understanding of cinematic multi-shot

---

Published as a conference paper at ICLR 2026
CogVideoX
Hunyuan
Hunyuan+Cinemarron No transition
Hunyuan Uncontrolled
Wanx2. 1-t2v-turbo
Iahozži
Ichor
Jphtt11
Shot midntteppatattion
StarDDifuuioon-CogViieexxz
consistency
Hunyuun-Cinematron
consistency
CogVideoX
Echoc
Isholde
(shos1)
Ours Training-inee 1.55
3.5s
Ours
35
6s
8s
Ishot
A ash-blond
angular cheekbones walks through dusk-lit plaza. (shot count: 3)
sunset ocean panorama gradually shifts warm close-up of sunlit clouds. (shot count: 3)
Figure 5: Qualitative results for different methods. Our proposed CineTrans outperforms others transition control while preserving coherence between shots, aligning with film-editing styles. The figure illustrates the shot segmentation results and specified shot count.
sparkling gown snowy peaks. (shot count: 2)
Giselle ball gown wanders sunlit forest. amidst fluttering birds. (shot count: 2)
Figure 6: Results of transferring our method to the customization model. It enables multi-shot video generation while maintaining consistent character identity.
sequences. Second, unmasked layers enable full-frame token attention, allowing each token to attend others across different shots, thereby establishing high-level semantic consistency. This facilitates the effective utilization of multi-shot video datasets, thereby generating multi-shot videos that align with film editing. We present the impact of masking different layers in Appendix D.2.
4.4 IMPLEMENTATIONS
Visible-First-Fraz Attention. Beyond the block-diagonal pattern detailed in Section 4.2, we additionally observe that, in certain attention layers, all visual tokens correlate strongly with the first temporal latent. This finding suggests a pronounced reliance on initial video information. To Siible-First-Framme mechanism within the attention layers,
exploit this observation, introduce augmenting the mask design from Section 4.3 to improve consistency multi-shot video generation. Further details provided in Appendix C.I.
Customization. Building on our mask mechanism, we guide a diffusion model originally designed for single-shot generation execute user-specified transitions, thus producing genuine multi-shot videos. For instance, by incorporating LoRA (Hu et al., 2022) weights, we achieve zero-shot gen- eration of multi-shot sequences with enhanced consistency and user-defined styles or character tributes, even though those weights are originally trained on single-shot videos.
EXPERIMENT
In this section, we present the implementation details, evaluation settings, and results. Section 5.1 describes the details of Cransrans and baselines, which evaluated series of metrics designed for the cinematic multi-shot video generation task. The metrics and results are presented in Sec- tion 5.2. Section 5.3 demonstrates that CranTrans performs well due to the components propose.

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative results. The best and rumner-up are in bold and underlined.
Method
Transition Control Scure
Semantic
Inter-shot Conristency
Visual
Consistency
Aesthetic Quality 1
Somantic Consistency
Subjectt
Backgroundy
Scweet
Cupp
Scoref
Gapl
Story Dillasionn
<CogVideoXI2V
0.5214
0,4966
0.5660
0.3605
0,9783
0.9713
0.6296
0.2091
VuneuanVides
0.3787
0
0.3764
0.6053
0.2855
0.9606
0.9721
0.5978
0.2082
-Cinematron
HunyuanVide Video
0.2111
0.5723
0.4075
0.5436
0.3485
0.9476
0,9633
0.6042
0.2064
Wanx2.1 1-T2Voturbo
0.2355
0.6431
0.3002
0,6516
0.2335
0.9332
0,9590
0.6324
0.2046
Cog VideoX
0.0324
0.5150
0.5915
0.6248
0.2226
0.9310
0.9582
0.5509
0.2061
CineTrans-Unct (Qurr)
0L8598
0.8095
0.2444
0.7247
0.1457
0.9598
0.9725
0.5747
0.2234
CinTTran--ITT (Qurs)
0.7003
017858
0.1552
0.1901
0.650N
Table 2: Quantitative results for ablation study. The best are in bold.
Method
Transition Score?
Semantic
Inter-shot Consistency
Visual
Intra-shot Consistency
Aesthetic Quality t
Semantic Consistency
Subject?
Background
Scweet
Cap!
Scoref
Gap
CineTTrans-UU Ablation
wio Mask, wio Tuning
0.9615
0,9702
0.5901
0.2110
wio Mark, w/ Tuning w/ Mask, wio Tuning CineTrans-Uns
0,2398
0.7900
0.3279
0.7226
0,2148
0,9582
0,9718
0.5711
0.2077
0.6168
0.7962
0,4336
0.8186
0.3000
0.9616
0.9719
0.5764
0.2196
0,8598
0,8095
0.3444
0,7247
0.1457
0.9598
0.9725
0.5747
0.2224
Cine Trans-DDD Ablation
wio Mask, w/o Tuning
0.2051
0.5924
0.3421
0.5274
0.3574
0.9153
0.9523
0.6322
0.2063
wro Mask, w/ Tuning Mask, Tuning
0.2112
0.6832
0,3422
0,6087
0.3312
0.9213
0.9526
0.6345
0.2019
0.7838
0.1772
0.7844
0.1943
0.6356
CineTrans-DIT
0.7013
0.7838
0.1552
0.7874
0.1901
0.9673
019975
0.6508
0.2109
5.1 IMPLEMENTATION DETAILS
We implement ThenTrans-Une based on Li (Wang et al., 2024b), and the mask mechanism is applied to the last six layers. We finetune the model on Cine250K with batch size of 128 for 20,000 steps and the learning rate is 1 x 10-1. Cine Cran--aaS extends Wan2.1-T2V-. 1.3B (Wan al., 2025) by integrating the mask mechanism applied to transformer layers 7-28 and is released in two variants. The training-free free variant augments the block-diagonal mask with the Visible-First Frame Attention (Section 4.4). The second variant further applics LoRA fine-tuning (rank=64) with batch size of 256 for 2,800 steps. All experiments are conducted on NVIDIA A100 GPUs. We also apply LoRA weights to CineTTrans- DiT for model customization, as shown in Figure 6.
For baseline comparison, select three categories: large-scale T2V diffusion model, multi-shot model, and customization model. Cog VideexX 1.5-5B (Yang et al., 2024), Hunyuan Video (Kong leverage large-scale pretraining and thus possess strong
al., 2024), and Wanx2.1-T2V-uuie mantic understanding. Each of these models is prompted with a general instruction specifying the desired shot count. StoryDiffusion (Zhou et al., 2024) first produces sequence of semantically consistent images followwng both the general prompt and individual shot-specific prompts, which XigVideoX12V (Yang al., 2024) then expands into a video. As a customization model, Cinema- tron' offers dedicated transition capabilities and employs the same sampling procedure as Hunyuan.
5.2 EVALUATION
For comprehensive evaluation, we design 100 hierarchical prompts with transitions using GPT-4o (Achiam et al., 2023), where each initial general description is annotated with shot count and then expanded into shot-level captions, thus constructing complete prompt. During evaluation, each
"https//tomgy.aliinn.ooo//// wtw.alaiwwwio.oo/aiiiiigggg/////
8

---

Published as a conference paper at ICLR 2026
Convergence of Vizual Consistency Distribution (Rackground)
Convergence Semantic Condistency Distribucion
Visual consistency distribution (Bar (Background)
Semantic
consistancy distribution
cencage barkii Coghhaee
Cur
Somple Siee
Sample Size
Inter-shat background consistency
Intenshot senontc consusency
(a) Convergence of consistency distribution.
(b) Consistency distribution for the methods.
Figure 7: Inter-shot consistency metric details and results. (a) As the number of samples in the reference set increases, the 95% CI Width and the cumulative mean of subset converge. indicating stability in the reference set's inter-shot consistency distribution. (b) Inter-shot consistency distri- butions of different methods, CraeTrras most closely matches that of the film-edited reference set.
method uses its required prompt format prompi or when using only the single general prompt, the shot count is explicitly included in the text.
To ensure fair comparison,
Metrics. We evaluate generated videos along three dimensions: transition control, temporal consis- tency, and overall quality. For transition control, we assess whether the shot count in the generated video matches the specified count using the Transition Control Score, computed as in Equation 5, where Sgenerated and Sspecified denote the shot counts in the generated video and the prompt, respec- tively, and k is a hyperparameter controlling the tolerance margin.
Sgenerated
(4)
Sspecifled
x
Transition Control Score
(5)
ck(ar-1)
Second, temporal consistency includes both intra- and inter-shot coherence. For intra-shot, follow- ing VBench (Huang et al., 2024), we compute subject and background consistency for each shot and average the results. For inter-shot, we consider both semantics and visuals. Semantically, we compute cosine similarity of ViCLIP (Wang et al., 2023b) features across shots. Visually, following VBBecchLLng, we compute subject and background similarity between middle frame of shots and average them. Furthermore, discussed in Section 4.1, overly high inter-shot scores may reflect undesirable pixel-level similarity, which violates the prior of multi-shot video design. To address this limitation, we introduce Consistency Gap as an auxiliary metric, defined as the Jensen-Shanae Distance between the score distribution of generated videos and that of the reference set:
JSD(P. - 1 P
V P P+0) + -
1 E
P P+Q) P+2) 2
Figure 7a illustrates the convergence of the consistency distribution for the reference set, which con- sists of 1, 1,000 professionally edited multi-shot videos. This metric complements the raw consistency score by quantifying the deviation from natural film-editing style, thus providing more compre- hensive evaluation of video consistency. Finally, following VBench, we assess overall video quality using aesthetic quality and overall consistency, which also evaluate the visual and semantic aspects, respectively.
The quantitative comparison is shown in Table 1. Our proposed Cine achieves near- perfect cinematic transition control across all prompts. In terms of consistency, CineTrans achieves high scores and closely aligns with film-editing style, which is also shown in Figure 7b. Because aesthetic quality largely determined by the base model, Cine Trans-Unnt performs slightly worse in this regard, whereas CinnTrans-Dii exhibits superior results.
Results.
For qualitative results, as shown in Figure 5, we compare the generated results of different methods. Our method demonstrates a remarkably frame-level transition control capability while preserving coherence across different shots. Even without fine-tuning, Cine TinnsTras-DD -iT exhibits strong perfor- mance, demonstrating the transferability of the framework. In contrast, large-scale pretrained mod- els fail to adhere to specified shot counts or fully misinterpret the concept of cinematic transitions. Similarly, both customized models and existing multi-shot models show low temporal consistency.

---

Published as a conference paper at ICLR 2026
w/o Visible-First-Frame
w/ Visible-First-Foome
Initial Shot
w/o tuning
tuning
(a) Effects of Serble-First-Fiii Attention.
(b) Qualitative comparison of Tuning for 0.4 epoch.
Figure 8: Qualitative comparison of key components. (a) Applying the Visible-First-Frame At- tention in Dran--DD stabilizes frames. (b) Fine-tuning enhances consistency between shots.
Deformation -bssed smooth transition
Fade-in/tade-out smooth transition
Figure Smoother transitions achieved through soft masking.
5.3 ABLATION STUDIES
We conduct ablation studies assess the impact of the mask mechanism and fine-tuning process. The results in Table 2 demonstrate the effectiveness of our components and the reasonable perfor- mance of the training-free variant. In terms of inter-shot visual consistency, the fine-tuned model yields a lower Consistency Score, reflecting increased compositional variation between shots intro- duced by training on film-edited multi-shot videos. This effect is further corroborated by reduced Consistency Gap, indicating closer alignment with the film-editing style. The slight decline in Aes- thetic Quality after fine-tuning may be attributed aesthetic domain differences between Cine250K and the original training set of base model. Furthermore, Figure 8 illustrates how fine-tuning and Visible-Firss First-FFrannn Frame Attention enhance video consistency and enable stable generation.
Additionally, since hard cuts represent the majority of cinematic transitions (99.58%), we implement shot transitions using hard masks. However, there remains need to explore soft mask strategies to achieve smoother multi-shot video generation, including seamless transitions between frames without visual breaks, as well as fade-in/fade-on effects. We present preliminary results in Figure 9, with further discussion in Appendix D.5. Soft masks improve transition smoothness and consistency but reduce shot transition control, introducing gap compared to cinematic transitions. Thus, hard masks remain the primary method for shot transitions. Nonetheless, the promising results of the soft mask approach highlight Trans's flexibility and robustness, demonstrating its ability to adapt to wider range of transition styles.
CONCLUSION
In this paper, we introduce a novel framework CineTrans for einematic multi-shot video genera- tion and construct comprehensive dataset Cine250K with detailed shot annotations. Through the analysis of attention maps in video diffusion models, we identify strong connection between tention probabilities and cinematic transitions. Based on this observation, we propose novel mask mechanism that enables fine-grained control over cinematic transitions, thus leading to CineTrans framework which transfers successfully in training-free setting. Extensive experiments validate the effectiveness of Cine Trans across multiple evaluation metrics, demonstrating improved transi- tion control, temporal consistency, and overall video quality. Our work demonstrates the potential of diffusion models for multi-shot video generation, offering new perspective for directly generating movie-like videos and paving the way for future research controllable video synthesis.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENT
The work was supported by Shanghai Artificial Intelligence Laboratory P1-PRI24ARC001- 20082025, We gratefully acknowledge the support and resources provided, which made this research possible.
ETHICS STATEMENT
This work makes use of video data collected from the publicly available Vimeo platform. We only videos that publicly accessible and do not require authentication bypassing of any access restrictions. The dataset is constructed solely for research purposes, in compliance with Vimeo's Terms of Service, and will not be used for any commercial applications. To mitigate potential privacy concerns, we avoid videos with personally identifiable information and exclude sensitive content. We will not redistribute the raw videos; instead, we provide scripts that allow others to download the data directly from Vimeo if permitted by the platform.
REPRODUCIBILITY STATEMENT
We have made significant efforts to ensure the reproducibility our work. The source code is provided in the supplementary materials. The dataset construction procedure is described in detail Section and Appendix A, which enables other researchers to replicate the data preprocessing pipeline. Together, these resources intended to facilitate the reproduction and verification of experimental results.
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 2023.
Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman, Frozen in time: joint video and image encoder for end-to-end retrieval. In International Conference Computer Vision, pp- 1728-1738, 2021.
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. Beit: Bert pre-training of image transformers. In International Conference on Learning Representations, 2022.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15122 2023.
Minghong Cai, Xiaodong Cun, Xiaoyu Li, Wenze Liu, Zhaoyang Zhang, Yong Zhang, Ying Shan, and Xiangyu Yue. Ditetrl: Exploring attention control in multi-modal diffusion transformer for tuning-free free multi multi-prompt longer video generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7763-7772, 2025.
Brandon Castellano. Pyscenedetect: Intelligent scene-cut detection and video splitting tool (version 0,6.4). Python package and tool, 2024. URL https:// github.com/ com/ TreadttrougidPySSeeeeeeeet PyScnnDeeecc.. Release version 0.6.4 (June 10, 2024).
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pp. 11115-13325, 2022.
Haoxin Chen. Menghan Xia, Yingqing He, Yong Zhang, Xiaodong Cun, Shaoshu Yang, Jinbo Xing, Yaofang Liu, Qifeng Chen, Xintao Wang, et al. Videocrafterl: Open diffusion models for high- quality video generation. arXiv preprint 2310 19512, 2023a.

---

Published as a conference paper at ICLR 2026
Haoxin Chen, Yong Zhang, Xiaodong Cun, Menghan Xia, Xintao Wang, Chao Weng, and Ying Shan. Videocrafter2: Overcoming data limitations for high-quality video diffusion models. In Computer Vision and Pattern Recognition, pp. 7310-7320, 2024a.
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wai wei Chao, Byung Eun Jeon, Yuwei Fang, Hsin- Ying Lee, Jian Ren, Ming-Hsuan Hsuan Yang, et al. Panda-70m: Captioning 70m videos with multiple cross-modality teachers. In Computer Vision and Pattern Recognition, pp. 13320-13331, 2024b.
Xinyuan Chen, Yaohui Wang, Lingjun Zhang, Shaobin Zhuang, Xin Ma, Jiashuo Yu, Yali Wang, Dahua Lin, Yu Qiao, and Ziwei Liu. Seine: Short-to-long video diffusion model for generative transition and prediction. In ICLR, 2023b.
Joseph Cho, Fachrina Dewi Puspitasari, Sheng Zheng, Jingyao Zheng, Lik-Hang Lee, Toe-Ho Kim, Choong Seon Hong, and Chaoning Zhang. Sora as an agi world model? a complete survey on text-to-video generation. arXiv preprint 2rXiv:2403.05131, 2024.
Karan Dalal, Daniel Koceja, Jiarui Xu, Yue Zhao, Shihao Han, Ka Chun Cheung, Jan Kautz, Yejin Choi, Yu Sun, and Xiaolong Wang. One-minute video generation with test-time training, In Pro- ceedings of the Computer Vision and Pattern Recognition Conference, pp. 17702-17711 2025.
Yuki Endo. Masked-attention diffusion guidance for spatially controlling text-to-image generation. The visual computer, 40(9):6033 6045, 2024.
Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Computer Vision and Pattern Recognition, pp. 151100-1519,, 2023.
Yuwei Guo, Ceyuan Yang, Ziyan Yang, Zhibei Ma, Zhijie Lin, Zhenheng Yang, Dahua Lin, and Lu Jiang. Long context tuning for video generation. arXiv preprint arXiv:2503. 10589, 2025.
Mingfei Han, Linjie Yang, Xiaojun Chang, and Heng Wang. Shot2story20k: A new benchmark for comprehensive understanding of multi-shot videos. arXiv preprint 2iv.22312.10300 2023.
Yingqing He, Tianyu Yang, Yong Zhang, Ying Shan, and Qifeng Chen. Latent video diffusion models for high-fdellity long video generation. arXiv preprint arXiv:2211.13221, 2022.
Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong. Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, al. Animate-a-stun Storytelling with retrieval- augmented video generation. arXiv preprint arXiv:2307.069400 2023.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Neural Information Processing Systems, volume 33, pp. 6840-6851, 2020.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR, 1(2):3, 2022.
Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianx- ing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Computer Vision and Pattern Recognition, pp. 21807-21818, 2024.
Xuan Ju, Yiming Gao, Zhaoyang Zhang, Ziyang Yuan, Xintao Wang, Ailing Zeng, Yu Xiong, Qiang Xu, and Ying Shan. Miradata: large-scale video dataset with long durations and structured captions. In Neural Information Processing Systems, volume 37, pp.
2025.
Ozgur Kara, Krishna Kumar Singh, Feng Liu, Duygu Ceylan, James M Rehg, and Tobias Hinz. Shotadapter: Text-to- moat-to-mlti-ssot video generation with diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 18405-28415., 2025.
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint 13200224/2.03603, 2024.
12

---

Published as a conference paper at ICLR 2026
Chengxuan Li, Di Huang, Zeyu Lu, Yang Xiao, Qingqi Pei, and Lei Bai. A survey on long video generation: Challenges, methods, and prospects. arXiv preprint arXiv:2443. 2024.
Xin Li, Wenqing Chu, Ye Wu, Weihang Yuan, Fanglong Liu, Qi Zhang, Fu Li, Haocheng Feng. Errui Ding, and Jingdong Wang. Videogen: A reference-guided latent diffusion approach for high definition text-to-video generation. arXiv preprint arXiv:2309.00334 2023.
Yumeng Li, William Beluch, Margret Keuper, Dan Zhang, and Anna Khoreva. Vstar: Generative temporal nursing for longer dynamic video synthesis. In Thirteenth International Conference on Learning Representations. OpenReview. net, 2025.
Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Yc, Shenghai Yuan, Liuhan Chen, al. Open-sora plan: Open-source large video generation model. arXiv preprint arXiv:2412.0013/, 2024.
Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. Llava-next: Improved reasoning, ocr, and world knowledge, January 2024. URL https://Ulavavvi. github.io/blog 2024-01 -0---00--00 llava-next/.
Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, and Tieniu Tan. Notice of removal: Videofusion: Decomposed diffusion models for high-quality video generation. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10209-10218, IEEE, 2023.
Antoine Miech, Dimitri Zhukov, Jean- Saanttat Alayrae, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howtol00m: Learning text-video embedding by watching hundred million narrated video clips. In International Conference on Computer Vision, pp. 2630-2640, 2019.
Kepan Nan, Rui Xie, Penghao Zhou, Tichan Fan, Zhenheng Yang, Zhijie Chen, Xiang Li, Jian Yang. and Ying Tai. Openvid- A large-scale high-quality dataset for text-to-video generation. arXiv preprint arXiv:2407.000 022.77.,,,, 2024.
Yichen Ouyang, Hao Zhao, Gaoang Wang, al. Flexifilm: Long video generation with flexible conditions. arXiv preprint arXiv:2404. 18620, 2024.
Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha, Ann Lee, Apoorv Vyas, Bowen Shi, Chih- Yao Ma, Ching- Yao Chuang, et al. Movie gen: A cast of media founda- tion models. arXiv preprint 2rXiv:2410.13720 2024.
Tianhao Qi. Jianlong Yuan, Wanquan Feng. Shancheng Fang, Jiawei Liu, SiYu Zhou, Qian He, Hongtao Xie, and Yongdong Zhang. Mask 2dit: Dual mask-based diffusion transformer for multi-scene long video generation. In Proceedings of the Computer Vision and Pattern Recogni- tion Conference, pp. 18837-18846, 2025.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Computer Vision and Pattern Recog- nition, pp. 10684-10695, 2022.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations, a.
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In Interna- tional Conference on Learning Representations,
Tomás Soucek and Jakub Lokoc. Transnet v2: An effective deep network architecture for fast shot transition detection. ACM International Conference on Multimedia, pp. 11218-11221, 2024.
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data- efficient learners for self-supervised video pre-training. Advances in neural information process- ing systems, 35: 10010000811999 2022.
13

---

Published as a conference paper at ICLR 2026
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention all you need. In Neural Information Processing Systems, volume 30, 2017.
Team Wan, Ang Wang. Baole Ai, Bin Wen, Chaojie Mao, Chen-Wet Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. et al. Wan: Open and advanced large-scale video generative models. arXiv preprint PrXiv:2503.203144 2025.
Ruichen Wang, Zekang Chen, Chen Chen, Jian Ma, Haonan Lu, and Xiaodong Lin. Compositional text-to-image synthesis with attention map control of diffusion models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 5544-5552, 2024a.
Wenjing Wang, Huan Yang. Zixi Tuo, Huiguo He, Junchen Zhu, Jianlong Fu, and Jiaying Liu. Videofactory: Swap attention in spatiotemporal diffusions for text-to-video generation. arXiv preprint arXiv:2305. 10874, 2023a.
Yaohui Wang. Xinyuan Chen, Xin Ma, Shangchen Zhou, Ziqi Huang, Yi Wang, Ceyuan Yang, Yinan He, Jiashuo Yu, Peiqing Yang, et al. Lavie: High-quality video generation with cascaded latent diffusion models. International Journal of Compuler Vision, pp. 1-20, 2024b.
Yi Wang. Yinan He, Yizhuo Li, Kunchang Li, Jiashuo Yu, Xin Ma, Xinhao Li, Guo Chen, Xinyuan Chen, Yaohui Wang, et al. Internvid: A large-scale video-text dataset for multimodal understand- ing and generation. arXiv preprint arXiv:2307.0644 2023b.
Zhifei Xie, Daniel Tang, Dingwei Tan, Jacques Klein, Tegawend F Bissyand, and Saad Ezzini. Dreamfactory: Pioneering multi-scene long video generation with a multi-agent framework. arXiv preprint arXiv:2408. 1788, 2024.
Tianwei Xiong, Yuqing Wang, Daquan Zhou, Zhijie Lin, Jiashi Feng, and Xihui Liu. Lvd-2m: A long-take video dataset with temporally dense captions. In Neural Information Processing Systems, volume 37, pp. 16623-16644, 2025.
Jiaqi Xu, Xinyi Zou, Kunzhe Huang, Yunkuo Chen, Bo Liu, MengLi Cheng, Xing Shi, and Jun Huang. Easyanimate: A high-performance long video generation method based on transformer architecture. arXiv preprint 2rXiv:2405.1890 2024.
Hongwei Xue, Tiankai Hang, Yanhong Zeng, Yuchong Sun, Bei Liu, Huan Yang, Jianlong Fu, and Baining Guo. Advancing high-resolution video-language representation with large-scale video transcriptions. Computer Vision and Pattern Recognition, pp. 5036-5045, 2022.
Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng. al. Cogvideox: Text-to-video diffusion models with an expert transformer. arXiv preprint 210::2200000072. 2024.
Lijun Yu, Yong Cheng, Kihyuk Sohn, José Lezama, Han Zhang, Huiwen Chang, Alexander G Hauptmann, Ming-Hsuan Yang, Yuan Hao, Irfan Essa, et al. Magvit: Masked generative video transformer. In Proceedings the IEEE/CVF Conference Computer Vision and Pattern Recog- nition, pp. 10459-10469, 2023.
Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Neural Information Processing Systems, 34:23634 23651, 2021.
Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint axXiv:24100027. 13, 2024.
Canyu Zhao, Mingyu Liu, Wen Wang, Weihua Chen, Fan Wang, Hao Chen, Bo Zhang, and Chunhua Shen. Moviedreamer: Hierarchical generation for coherent long visual sequence. arXiv preprint arXiv:2407. 16655, 2024.
Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma. Yexin Liu, Wenjie Shu, Yatian Pang, Fei- long Tang, Qifeng Chen, Harry Yang, et al. of A collaborative framework for multi-shot video generation. arXiv preprint o2Xiv:2412.002259 2024.
14

---

Published as a conference paper at ICLR 2026
Yupeng Zhou, Daquan Zhou, Ming-Ming Cheng, Jiashi Feng, and Qibin Hou. Storydiffusion: Con- sistent self-attention for long- rrange image and video generation. Advances in Neural Information Processing Systems, 37:1 10001-11113000 2024.
Shaobin Zhuang, Kunchang Li, Xinyuan Chen, Yaohui Wang, Ziwei Liu, Yu Qiao, and Yali Wang. Vlogger: Make your dream vlog. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8806-881111 June 2024.
15

---

Contents
Introduction
2 Related Work
3 Dataset
Methodology
4.1 Preliminary
4.2 Frame correlation in attention module
4.3
Mask mechanism
4.4
Implementations
5
Experiment
5.1
Implementation details
5.2
Evaluation
8
5.3
Ablation Studies
10
Conclusion
10
A Details of Dataset Construction
17
A.I Method of Splitting and Shot Segementation
17
A.2 Details of Stitching Phase
18
B Statistic of Cine250K
18
Additional Details of Implementation
20
C.I Visible-First-Framee Attention
20
C.2 Multi-prompt
................................
21
C.3 Transition Point Selection
21
Additional Results
22
D.1 The Specific Details of the Attention Probabilities.
22
D.2 Impact of Mask Layers on Results
25
D.3
Qualitative Evaluation for Ablation Studies
26
D.4
User Study
28
D.5
Achieving Smoother Transitions through Soft Masking
28
D.6
Overlapping Ambiguous Shot Boundaries
29
Evaluation
30
E.1 Prompt Details
30
E.2 Metric details
30
Limitation
33
F.1 Failure Case
33
F2 Future Work
33
G Use of Large Language Models
33
16

---

Published as a conference paper at ICLR 2026
A DETAILS OF DATASET CONSTRUCTION
Section 3 describes the Cine250K construction pipeline. this section, we provide a detailed account of different stage.
A.1 METHOD OF SPLITTING AND SHOT SEGEMENTATION
During the Splitting stage, videos are initially segmented using PySceneDetect (Castellano, 2024). In the subsequent gradual-change removal stage, TransNetV2 (Soucek & Lokoc, 2024) is employed to detect and handle gradual transitions, yielding the final shot boundaries and associated shot la- bels. This pipeline is selected because Pyscenedetect operates exclusively the CPU, offering greater processing efficiency than Transnetv2; however, for a pre-segmented video, Transnetv2 achieves higher shot segmentation accuracy and can handle gradual transitions more effectively than Pyscenedetect.
For Transnetv2, Figure 10 presents an example of detection. Transnetv2 provides single-frame- prediction and all-frame all-frame-rrediction which are designed to handle hard cuts and gradual transitions, respectively. The salee-frame-rrddiictt transition. In contrast, the all-frame-pre all-frrmm-rreiiciion predicts all frames involved in transition, essen- tially estimating the probability of frame being part of gradual change. We apply threshold-based filtering to exclude frames with high probability of being transition frames and obtain shot labels. Table presents the threshold settings.
refers the probability of an individual frame being
Figure 10: An example of transition detection by Transnetv2. The green line represents the result of single-frume-preddctton,, while the blue line represents the result of allfframm-rreiitioon
Table 3: Threshold settings for dataset construction.
parameter
value
Pyscenedetect splitting
0.9
0.7
0.8
Transnetv2 frme-trramettteeseeeedd
0.45
Transnetv2 all-frame- threshold
0.50
Table 4: Shot Segmentation Accuracy of different methods.
Method
Pyscenedetect
Transnetv2
Shot Segmentation Accuracy
65.50%
87.00%
To quantitatively compare accuracy on shot segmentation, we randomly select 200 videos cover- ing multiple categories, each containing multiple shots. We then apply both Pyscenedetect and Transnety2 for shot segmentation and compare the results manually. A correctly segmented video is assigned 1; otherwise, O. The shot segmentation accuracy is then calculated accordingly, as demon- strated in Table 4. A specific example is shown in Figure 11.
17

---

Published as a conference paper at ICLR 2026
Transnetv22 Pyszenadeee
Shot
Figure 11: An example of shot segmentation by Pyscenedetect and Transnetv2. In the figure, Transnetv2, after the removing gradual changes step, accurately segments the two shots, while Pyscenedetect fails to identify the gradual changes. The transition frames caused by gradual changes are marked with dashed lines.
A.2 DETAILS OF STITCHING PHASE
During the Stitching stage, adjacent segments exhibiting high semantic similarity are merged to form single video, thereby constructing multi-shot video collections. Specifically, we extract semantic features from the first and last frames of each segment using ImageBind (Girdhar et al., 2023) and quantify their similarity via Euclidean distance. For the i-th segment C we denote the semantic features of its first and last frames Cirst and C respectively, with their distance represented as dis ( first Cama). Based the distance, video segments are processed sequentially and either stitched or filtered according to the following criteria.
For a segment C+ if dis (iisstrss Gend) > a, the segment filtered.
For O*, if Ci-1 is absent (either non-existent or filtered), C is treated video's begin- ning.
For C³, if dis(Cind Chra) < B and dis(Crrd Cri-1 first: Ca < 7, then Ci-1 and C are stitched to form a new segment.
Here, a restricts significant changes within a clip, 3 enables stitching of semantically similar tran- sitions or originally continuous segments, and ensures consistency between the video's 's beginning and end. After processing all segments sequentially, each resulting segment is considered a complete video, forming preliminary dataset of videos with shot transitions.
B STATISTIC OF CINE250K
As we present in Section 3, Cine250K is a carefully curated multi-shot video dataset with detailed captions. This section presents its overall statistics. As shown in Figure 12, the average video duration is 10.75s, and the average caption length 148.79. Most videos contain 2 to 3 shots. is important to note that although duration and shot count filtering are applied during data processing, TransnetV2 (Soucek & Lokoc, 2024) is later utilized remove gradual changes and re-identify shots. As result, the final shot count and duration do not strictly adhere to the initial filtering criteria. After reidentification, 87.99% of the videos contain shots. Figure 12 presents the shot distribution for videos with 1 to 10 shots, which represents 99.90% of all videos.
Regarding video categories, following Vimeo's classification, the dataset is divided into cate- gories. Among them, Travel and Documentary have relatively higher proportions. Overall, the distribution categories is fairly balanced.
In Figure 13, select several example vidco-text pairs to demonstrate the specific characteristics of the cinematic multi-shot sequences in the dataset and the style of captions. In Table 5, we compare Cine250K with other datasets. As multi-shot video dataset with detailed shot labels, the high- quality content of Cine250K can significantly facilitate research and exploration in multi-shot video generation.
Additionally, when Cine250K is used as the training dataset for CineTrans, the distribution of tran- sition point positions and shot lengths is relatively uniform (Figure 14), which enables Cine
18

---

Published as a conference paper at ICLR 2026
Table 5: Comparison of Cine250K and other video-text datasets.
#Videus
Avg
Avg
Multi-shut
label
Aesthetic
Resolution
InterVVi (Wang et al., 20236)
234M
11.7s
17.6
High
720P
LVD-2M (Xiong et at. 2025)
2M
20.2s
88.7
Medium
Diverse
OpenVid-1NN -M (Nan et al,, 2024)
1M
N/A
N/A
High
Diverse
Panda70M (Chen al., 2024b)
70.8M
8.5%
13.2
x
Medium
720P
LLaVA Video-1 178K (Zhang al,, 2024)
178K
40.4s
~ 300
High
Diverse
Shoi2Story20K (Man et al,, 2023)
20K
16s
201.8
Medium
720P
Shor2Story 134K
134K
N/A
N/A
Medium
720P
Cime25KK (Ours)
250K
10.75s
148.79
/
/
High
720P
Video Duration Visualization
Shot Count Visualization
30K
120K
Video Namber
80K
Video
15K
60K
10K
20K
5K
10
Shot Count
Durstion(s)
Category Distribution
Video Aesthetic Score Visualization
Documentary
80K
70K
Music
Travel
Video Number
60K
14.3%
50K
40K
Branded Content
30K
Ads and Conmercal'
11.0%
20K
Experimental
9.7%
10K
Sporte
Nornatize 7.4%
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
8.5
Aesthetic Score
Anteattin
Caption Word Count Visualization
50K
Video Number
30K
20K
Word Count
Figure 12: Statisties of Cine250K. To facilitate observation, the figure presents the shot distribution for vidcos containing to 10 shots. The caption word count distribution only considers data with fewer than 500 words.
19

---

Published as a conference paper at ICLR 2026
The video showcases sereme and picturesque setting featurina small, red wooden cabin nestled amidst lush greenery. The cabin, with its sleeply pitched roof and chimney, surrounded by dense foliage, including various trees and shruas, crealing a sense of seclusion and tranqui lity. clear lake is visible the background, reflecting the surrounding landszope and adding to the peaceful ambiance. The sunlight fllters through leaves, casting dappled shodows on the ground and highlighting the vibrant colors of the foliage. The
The video begins with wide perial view of lush valley surrounded rallina hills and mountains, A stone viaduct with multiple arches stretches across the valley, and red train is the
The train slowly from left to right, crossing viaduct moving out of the frome. The background features clear blue sky with few scattered clouds,
traveling
landscape vibrant
greenery. The scene transitions to closer cerial red
of the it crosses stone vioduct. The train continues its journey from left to right, maintaining steady
clearly visible
shifts slightly, offering different perspectives the cabin and its surroundings.
The surrounding landscape remains lush and green, with rolling hills and mountains background under cleur blue sky, The video canc lucee with the still motion, gradually moving out the frame.
emphasizing natural beauty and calmness the location.
The video fectures live musical performance on stage,
group musicians dressed in vintage-style suita and hots playing various instruments. The central figure, wearing gray pinstripe suit, white shirt, red tle, and gray fedora hat, la to musician is playing a saxophone, while a third musician is playing trombonne. The lighting, creating an intimate atmasphere. A banner with the
The video features a red clossic muscle car driving on road during the late afternaon with headlights and license plate reading 'DL 75721'. '. seen moves forward. The and natural elementa, such as buildings, trees, and mountains under partly cloudy sky. The glow the enhoncing the visual appeal. The car's sleek des and shiny exterior are highlighted as through the showcasing its smooth motion and the picturesque surroundings.
evening.
holding microphone und
singing speaking. Behind
from various angles
background includes
stage is dimly with
text 'music is life' visible in the
background, emphasizing the theme the performance: The musicians are deeply engaged in their performance, contributing overall ambiance swing band concert.
jazz or
Figure 13: Example video-text pairs in Cine250K.
Distribution
Length
Distribution Transition Point
0.06
0.06
0.04
Rato
0.04
0.02
0.02
0.00
0.00
10
15
20
10
15
20
Shot Length ( (Temporal Stice)
Temporal Slice Index
(b)
Distribution of shot length and transition points in Cine250K as the training dataset. Given the temporal compression the VAE encoder, temporal slices are used as the unit.
Figure 14:
achieve precise frame-leve control without timestamp jitter and ensure stability when varying shot lengths are used as conditions.
C ADDITIONAL DETAILS OF IMPLEMENTATION
C. VISIBLE-FIRST-FRAMD ATTENTION
In Section 4.4, we introduce the implementation detail dubbed Visible-First-Famme Attention, which serves to further stabilize the mask mechanism, particularly within DiT architectures, as demon- strated in Figure 8a. This mechanism is also motivated by our analysis of DiT's attention maps during multi-shot video generation. As illustrated in Figure 15, in certain layers all visual tokens assign high attention probabilities to tokens originating from the first frame (owing to temporal compression in the VAE, this in fact corresponds to the first latent temporal slice), suggesting that the initial frame assumes special function in the diffusion model's denoising process. In light of
20

---

Published as a conference paper at ICLR 2026
Layer14 14
Layer
Layer22
Layer
Timestep15
Timestep25
Timestep35
Figure 15: Visualization of the temporal-domain attention maps of Wan2.1 when generating multi- shot videos. Certain layers exhibit a pronounced focus on the first temporal slice, which motivates Visible-Finst-Frame Attention mechanism.
this observation, we modify our mask matrix so that its first column is set entirely to zero, thereby effecting the VisibleFFirst-ranme Attention mechanism, which can be formulated as:
if = 1 ir E same shot
Mi
(7)
-oc
if + 1 or i & same shot
C.2 MULTI-PROMPT
In Section 5.2, note that some methods employ multi-prompt strategy inference, i.e., each shot prompted by its own text description. CineTrrans-Di' also supports this capability. In addition the primary mask matrix between video tokens, we introduce an additional mask between text and video tokens, following Qi et al. (2025), to enable precise semantic control over each shot. It worth noting that this mask applied only the attention layers governing text-video token interactions, and is distinct from our proposed mask mechanism, which operates on video-video token interactions.
C.3 TRANSITION POINT SELECTION
In Section 4.3, we introduce the Mask Mechanism that achieves temporal control of shot transitions. During evaluation, shot transition points should be predefined in advance to generate multi-shot videos. To address this, we predefine several suitable shot transition points for different shot counts and randomly select them during inference. However, mentioned in Appendix B, our training set adequately supports shot transitions at various positions, so random sampling of transition points does not lead to issues such visual collapse (Figure 16). Nevertheless, for optimal visual perfor- mance, including action completeness and overall video quality, uniform shot lengths are preferred, they avoid extremely short shots.
21

---

Published as a conference paper at ICLR 2026
Figure 16: Multi- shot videos with shorter length for last two shots. It can be observed that the vi- sual quality remains intact even when the shot lengths are relatively extreme.
D ADDITIONAL RESULTS
D.1 THE SPECIFIC DETAILS OF THE ATTENTION PROBABILITIES.
In Section 4.2, we explore the frame correlations in the case of cinematic multi-shot video generation in diffusion models, where attention modules in certain layers exhibit block-diagonal structure, i.e., strong correlations for intra-shot frames and weak correlations for inter-shot frames. In this section, we will discuss the specific details of the attention maps across different architectures, layers, and timesteps.
As for the architecture, we investigate both the attention framework. The module directly the time sequences, where tokens different spatial locations do not interact with each other. In contrast, the full attention framework operates on all tokens of the video, al- lowing correlations across both temporal and spatial dimensions simultaneously. To focus on frame correlations, group and average the tokens in the full attention framework by the frames before conducting further analysis. In Figure 17 and Figure 18, we use Wang et al. (2024b) and Kong et al. (2024) representatives of different architectures and present the attention maps across different layers and timesteps in both qualitative and quantitative manners.
framework and the full framework applies the temporal attention
As timesteps increase, the discrepancy between intra-shot and inter-shot attention probabilities grows in the cemporal--patial-dccooppp tion framework. For different layers, the temporal--patial-decouupp framework exhibits noticeable differences across most layers, whereas the full attention framework shows disparity primarily in the earlier layers. In summary, both frameworks demonstrate significant differences between intra- shot and inter-shot attention probabilities. This finding further substantiates the prevalence of strong intra-shot and weak inter-shot correlations, supporting the proposed approach.
framework, whereas it remains stable in the full atten-
Additionally, we present the inter-shot/intra-sher attention probability ratio for different models and layers, as well as the Pearson correlation between this ratio and the distance from transition points (i.e., whether the ratio increases as partition points approach true transitions), with results recorded in Table 6. can be observed that the inter-sho//nntr--she ratio tends to increase as partition points approach the transition points across differrnt models, further supporting the general presence of the block-diagonal pattern diffusion models.
Finally, we investigate the potential cause of this pattern by comparing two cases with identical conditions but different seeds: one exhibiting a shot transition and the other only a camera movement (Figure 19). Attention map analysis in Table 7 shows that the case with a shot transition exhibits stronger block- -iiao-da pattern, further proving that this attention pattern is strongly correlated with the diffusion model's understanding of shot transitions.
22

---

Published as a conference paper at ICLR 2026
Froteca
10
20
Layer
Layer
Layer?
Laver
Lower 13
Laver
Lager
Loyer
Lager
Luyer
Loper
Logar
Loyer
Layer
Layer 15
16
Layar 40
Laysr 50
Layer 50
(a) Visualization of temporal attention maps in catppoal--pptil-deccooee framework. The attention probability matrices across most layers exhibit dlok--diagonal pattern.
(b) Visualization of the averaged attention maps for visual tokens, grouped by frame, in the full attention framework. Earlier layers tend to hibit Hlock-diagonl pattern.
The visualization of attention maps between visual tokens from different frames for individual case of multi-shot video generation. Both the - and full attention framework, diffusion models exhibit strong intra-shot attention and weak inter- shot attention in certain layers.
Figure 17:
framework
Stop 1D
Step 40
Cintro-hhct
Clinansshor
Ciitre-shot ginneesshor
Dnttroaanor Cintenahot
0005
0,00
3.03
1.02
E01
001
6.02
Layer1
Layert
Layer?
Lapert0 Layer1 Laperto
Layer1
Layerd
Layer?
Layer
Layert'
Loper16
Layer1
Loyerd
Layer?
Layertc Laper13 Layertd
(a) Result of temporal attention probabilities in camporal-spatial -ecouppec framework. most layers, the probabilities of tokens within shot and those across shots exhibit noticcable difference, which seems to increase as the denoising process progresses.
Step
Bintroshot Cinttr shot
Stop 25
Step 40
Dirtr-ssht #lintenshht
0.35
6.0
668
0015
55%
0.06
Laver10
Laver20
Lover30
Loyenbo
LoverBd
(b) Result of attention probabilities for visual tokens in full attention framework. The difference between the probabilities of tokens within shot and across shots remains relatively stable during the denoising process, with a tendency exhibit larger disparity the earlier layers.
The average attention probability for intra-shot and inter-shot across diffusion layers and denoising steps. In both framework, the average probability within shots is obvious greater than that between shots for certain layers.
Figure 18:
23

---

Published as a conference paper at ICLR 2026
Camera Move
Shot Transition
Figure 19: Comparison of attention maps between cases with identical conditions but different seeds: one exhibiting a shot transition and the other only showing camera movement.
Table 6: Inter-shot/Intra--hot attention probability ratio and Pearson correlation for different mod- els and layers.
Model
Intra-shat/lnter-shox attention probahility ratio
Pearson correlation
Wan2.2 (front layer 33%)
5.6792
0.9267.p<00.001
Wan2.2 (middle layer 33%)
7,4204
0,9233.p=c0.001
Wan2.2 (late layer 33%)
6.9483
0.8496.p<00.001
Hunyuan layer 335))
0.6571.p<00.0011
Hunyuan (middle layer 33%)
0.7507.p<000001
Hunyuan (late layer 33%)
1.5194
0.4969.p:c0.0001
Wan2.1 I (front layer 33%)
8.3925
0.7393,p-00.001
Wan2.1 (middle layer 33%)
14.1942
0.7319.p<00.000
Wan2.1 I (late layer 33%)
14,6686
0.6159.p=00.000
Table Inter-shot/nntra-soo attention probability ratio and Pearson correlation for two cases (with and without transition).
Model
Intm-shho/Tmner-shot attention probability ratio
Pearson correlation
Case with shot tramsition
Wan2.1 (Trent layer 33%)
8,3925
0.7393
Wan2.1 (middle layer 33%)
14,1942
0.7319,pc00.001
Wan2.1 (late layer 33%)
14.6686
0.6159.p=00.001
without transition
Wan2.1 (front layer 33%)
6.7575
0.3462,p<00.001
Wan2.1 (middle layer 33%)
4.9148
0.3378.p<00.001
Wan2.1 (late layer 33%)
6.7575
0.3462.p<00.001
24

---

Published as a conference paper at ICLR 2026
masking front layers
mask-free
masking middle layers
masking all layers
masking late loyers
(a) Results of CineTTran-UUne
masking layers 1-6
masking 1 11-21
masking all layers
masking layers
masking 29-30
severe visual distortion when masking layers
(b) Results CineTrans-DiT.
Figure 20: The results of different mask strategies. Applying the mask to the later layers of the senooll--tmoooa-eeooooe
Unet) and the middle layers of the full atten- tion architecture (CineTrans-Die is considered more effective. For the full attention architecture, applying the mask to all layers leads to severe visual distortion, as illustrated in the figure.
architecture
D.2 IMPACT OF MASK LAYERS ON RESULTS
For LineTrans-nne and CineTrans-DiT. the mask mechanism employs different masking layers, a design choice motivated by observations of the attention maps when diffusion models generate multi-shot videos. In this section, examine the effects of applying masks different layers the quality of the generated videos, thereby demonstrating the rationale behind our masking strategy.
As shown in Figure 20a, CineTrans-Unie Unet exhibits noticeable visual distortions when the mask is applicd to all layers or only to the early layers. In severe cases, some parts of the video become indistinct heavily degraded. When mask is applied when the mask is applied only to the
25

---

Published as a conference paper at ICLR 2026
Table Quantitivate results for masking different layers in new different frameworks. The best are in bold.
Method
Trunsition Control Scoret
Semantic
Inter-shot Consistency
Visual
Intru-shot Consistency
Aesthetic Quality?
Semannti Cousistencyt
Subjectf
Background
Scoret
Gapl
Scoret
Capl.
Ablation
VideoCrafter2 (Uneet
Mask front 1/2 layers
0.0889
0,8313
0.3630
0.8338
0.3603
0.9594
0.9721
0.6287
0.2205
Mask late 1/2 layers
0.1889
0,7739
0,1980
0,7766
0.3416
0,9728
0,9693
0.6258
0.2213
Ablation Wan2.2 (DiT)
Mask front 1/2 layers
0
0,9425
0.5977
0,9380
0.5205
0.9570
0.9624
0.6298
0.2073
Mark middle 1/2 layers
0.5982
0.6752
0.2084
0.7234
0.1458
0.9474
0,9636
0.6395
0.2140
Mark lase 1/2 layers
0.0085
0.9466
0.9644
0.6330
0,1943
Wan2.2
Mask front 3/4 loyors
VideoCrafter2
Mask front 16 layers
Mask loyers
late layers
Mask late 1s layers
Transition Point
Transition Point
Figure 21: Results of masking different layers in new different frameworks.
middle layers, cinematic transitions do not emerge clearly. Applying the mask to the later layers enables effective control over transitions without significantly affecting visual quality, suggesting architecture, correla- tions in the earlier layers primarily influence visual quality, while the later layers may regulate the consistency between adjacent frames. Therefore, applying the mask mechanism to the last six layers demonstrated to be effective.
as the optimal strategy. This suggests that, in the paatial-emporrl-decoope
As shown in Figure 20b, the proportion of layers requiring masking in CineTrrans-DiT is larger than in GineTTran--Ui and both the early and late layers need to retain fully visible attention. Masking all layers this architecture may lead to low inter-shot consistency and severe visual degradation at the transitions. Conversely, masking only the earlier or later layers fails to effectively guide the transitions. Moreover, when fewer layers are masked, the inter-shot differences are reduced, which deviates from the convention of multi-shot video. These observations suggest that the current strategy of masking the middle layers achieves the optimal balance, enabling precise control over transitions while preserving reasonable degree of consistency.
To validate the generalizability of our heuristic mask- layer selection, we apply the same approach to other architectures: masking the later half of layers in U-Net and the middle half of layers in DIT. We use VideoCrafter2 Chen et al. (2024a) and Wan2.2 Wan et al. (2025) as base models, with results presented in Table 8. Due to VideoCrafter2's ss relatively limited duration and representational capacity, the overall Transition Control Scores are relatively low. However, masking later layers still leads to clearer shot-transition behavior. Figure 21 shows that selecting the appropriate masking layer can indeed improve multi-sho video generation performance.
D.3 QUALITATIVE EVALUATION FOR ABLATION STUDIES
The quantitative results the ablation studies are presented in Section 5.3, and this section provides supplementary qualitative analysis.
26

---

Published as a conference paper at ICLR 2026
training. mask
bhott]
training.
No transition, Low Aesthetic
[shatt)
Shatt1
Ganoe11
15ho11
Ihort)
w/o training, w/ mask
[shatt]
[shott]
Ganoe1)
Iahat2
CineTrans-Unet (Ours)
Low Aesthetic
Whel
Bhe
delicate flower a pot illuminated by soft natural light, creating a calm and peaceful atmosphere. (shot count:
training, w/o mask
w/ training. w/o mask
No tranaition
w/o training, w/ mask
No transition
Whetl
Trans-DDD (Ours)
Consistency
ebett!
Shstil
(poort)
(cho2)
young girl with curly blonde in pigtails, her wide eyes filled with wonder.
Figure 22: Qualitative results of ablation studies.
27

---

Published as a conference paper at ICLR 2026
As shown in Figure 22, CineTTrans stably control the generation of transitions. In contrast, models without the mask mechanism, whether finetuned or not, are almost incapable of generating einematic transitions. Direct application of the mask mechanism without further finetuning, i.e., training-free results, does indeed generate transitions. However, further training can help the model produce shot transitions that better align with the film editing style, exhibiting higher aesthetic qual- ity and improved consistency.
D.4
USER STUDY
To complement the experimental results, we report the findings of user study in this section. Specifically, we select 20 prompts for user evaluation, where participants rate each result on scale from to 5. We evaluate different methods from the perspective of Transition Control and Overall Consistency, with the MOS results presented in Table 9. It can be observed that Cine Trans also demonstrates strong performance in terms of user preference.
Table 9: Results of User Study
Transition Control
Consistency
CineTraan-DDi (ours)
4.60 + 0.50
4.15 + 0.75
Trans- Unet (ours)
4.75 + 0.44
4,10 1 0.72
Story DinyDDiiisionn+CCgg CoVVeeeeX22V VideoX122
3,95 1: 0.76
Hunyuan Video=Cinennnn Cimenntron
1,95
HlnnyannVides
3.45 1: 1.88
3.50
1: 0.76
Cog VideaX
2.50 + 1.24
Wank2, I-T2V-ubbo
3.25 + 1.77
3,45 # 0.69
D.5 ACHIEVING SMOOTHER TRANSITIONS THROUGH SOFT MASKING
This section investigates soft masking strategies applied to the hard mask mechanism proposed in 4.3, with the aim of assessing whether soft masking can enable smoother shot transitions or higher consistency. Specifically, we explore strategies including time-dependent penalty and timestep- dependent penalty. The following will present the details of these strategies along with the corre- sponding experimental results.
Temeedependent Penalty. In the hard masking scheme, the mask matrix is initially set be fully invisible, allowing tokens within each shot to interact for multi-shot video generation. The time- dependent penalty allows token interactions near shot boundaries. Specifically, we initialize the mask matrix using a Gaussian decay based on the distance between frames:
My 5)=
(8) This is then mapped to the range [0, -oo), with tokens within each shot being fully visible sub- sequently, thus forming soft masking strategy around shot boundaries. The smoothness of the transition is controlled by the parameter σ. The evaluation results are presented in Table 10 and Figure 23, where 7. denotes the sequence length. As the soft mask approaches full visibility, the transition control effect weakens until no shot transition occurs. With the appropriate hyperparam- eter settings (e.g.,
202
L/12), the visual break the transition boundary becomes less obvious. However, excessively large o values lead to minimal difference across shot compositions, reducing the content diversity in the multi-shot video and resulting in more uniform appearance, which explains the decline in semantic consistency.
Deeeedep-Dependent Penalty. contrast, the Diffusion- Diffusion-Timestep- Siimestep-Dependert Penalty focuses on the timestep of the denoising process. making the invisible region of the mask partially visible during the early denoising steps and gradually approaching full invisibility as the process progresses. Compared to the Time- Dependent Penalty, which focuses on shot boundaries, this soft masking enhances interactions between all inter-shot tokens. As shown in Figure 24, the increased token correlations result in significant reduction in compositional differences between shots, leaving only a residual boundary transition effect. This comes at the cost multi-shot content diversity, reflected by the substantial drop in the Transition Control Score in Table 11. Therefore, the Penalty tends to strengthen inter-shot consistency, while its effect
Diffusion- biffusion-Timestep.. smoother transitions is less pronounced than that of the time-dependent penalty.
28

---

Published as a conference paper at ICLR 2026
Initialize
Attention Map
Transition Point
Hard Mask
block-
diogonal mask
a=L/12
o=L/6
Figure 23: An illustration of the Time- time-Dependent Penalty.
Table 10: Quantitative results for Time-Depender Penalty. The best are in bold. Smaller o values approach the hard mask.
Inter-shot Consistency
Intra-shot Consistency
Method
Control Score1
Semantic
Visual
Aesthetic Quality
Semantic Consistency?
Subjectt
Background
Sooret
Cap4
Score
Gapl
Ablation Cine
(training
L/4
0,0220
0,8685
0,4261
0.8231
0,2499
0.9629
0.9750
0.6513
0.2025
a
1,/6
0.0700
0,8238
0.2411
0.8248
0.2431
0.9445
0.9606
0.6514
0.2073
L/12
0.2139
0.2086
Hard Mask
0.6564
0.7838
0,1772
0.7844
0.1943
0,9618
0.9746
0.6556
0,2093
Ablation for CincTrans-DM Dil (trined)
a L/4
0.9541
0,9660
0.6453
0.2001
I./6
0.0455
0.7944
0.1885
0.8377
0,3074
0.9424
0.9598
0.6455
0.2105
L/12
0,5193
0.7902
0.1796
0.8145
0.2575
0.9519
0.9639
0.6489
0.2095
0.2109
D.6 OVERLAPPING AMBIGUOUS SHOT BOUNDARIES
The results in Table demonstrate that model maintains precise control over shot transitions at specified transition points using the mask mechanism. this section, we examine the model's performance under overlapping and ambiguous shot boundaries. Specifically, tuations of #/ frames (temporal slices) around the fixed transition points during inference, which generates overlapping and ambiguous boundaries in the denoising process. Quantitative results
introduce fluc-
Table Quantitative results for Samestep-Dependeen Penalty. The best bold. Smaller values approach the hard mask.
Inter-shot Consistency
Method
Transition Control Scoret
Semantic
Visual
Scoref
Gap.l
Scoref
Gapl
Ablation for CineTrras-DDT (triining-free)
x 1.0
* = 0.5
Hard Mask
0.6564
0.7838
0.1772
0.7844
0.1943
Ablation for CineTrans-DD DIT (trained)
== 1.0
0
1
0.0741
0.7864
0.2211
0.8029
0.3655
Mask
0.7003
0.1552
0.7874
29

---

Published as a conference paper at ICLR 2026
Hord Mask (==0)
preestepdeezendent (t-0.5)
Invisible Regigh
Tinecto
MI
demeste--eppendent Penalty (t-1.0)
Fimestep
Figure 24: An illustration the Timestep- -eahtedp-DDendnent Penalty.
Figure 25: A Result of Overlapping Ambiguous Shot Boundary.
shown in Table 12 and Figure 25. It can be observed that, even under overlapping and ambiguous shot boundaries, the model retains a reasonable Transition Control Score and produces effects sembling fade-in/fade-our transitions to some extent. These results confirm the robustness of the mask mechanism, demonstrating that the model retains effective control even in the presence of ambiguous or overlapping shot boundaries.
E EVALUATION
E. PROMPT DETAILS
In Section 5.2, we present a of prompts generated by GPT-4o (Achiam et al., 2023) evaluate the performance of multi-shot video generation. Figure 26a provides the prompt used to guide GPT.
Given the long inference time of video generation models, we design 100 prompts evaluation, covering multiple categories. For these prompts, the transition guidance can be divided into two types. One type explicitly specifies a significant semantic change, such as prompts that include phrases like The video transition to, clearly indicating shot changes. The other type implicitly guides the shot transition, where the prompt does not explicitly differentiate content changes be- tween shots, but rather provides overall description of the video. This type of prompt design accounts for the fact that some cinematic multi-shot videos only involve switching camera angles without significant semantic change. The multi-shot guidance in such cases primarily relies on the prompt's opening phrase: The multi-shot video consists of {num_shot} shots. Figure 26b presents examples of these two types of prompts, and Figure 26c shows the word cloud distribution for this set of prompts. Table 13 outlines the categories of the prompts. It is worth noting that, for methods re- quiring multi-prompt inference, we employ GPT-4o to expand the general prompt into shot-specific captions according to the designated shot count.
E.2 METRIC DETAILS
In Section 5.2, establish evaluation metrics from three aspects: transition control, temporal con- sistency, and overall video quality. This section will provide a detailed explanation of the specific definitions of these metrics.
30

---

Published as a conference paper at ICLR 2026
Zoomshe
acrosSeniphh
Promot for GPT 4o
Prompt
Fromot
OS
euo
videcs. You now requird
one part leam
video begins comfortabl living room, large
multishol video consists shots, expuraive lundin sirelching beneoth pale blue day, where the earth hard and sracted, sporse tufts grass cling to Snow-capped mountains on the nonton. their peots plencing the dky. wind
vybale Frund
scene
slowly
Dac
backleaves
captures
generate video desoript lons content with lens tranaitions sampling the genenction model.
soll, wurm light Windows, with pluhh furniture orranged cround sofa. The
Hooden artod
sky
she
inta
acene tronsitoons to a baokahelf filled with aseartment books, picture fromes, and decorative items. the video ends with shat the living room as night folls, the lights cim and peaceful atmosphere fills the space. whole vidco shows warm and comfortable scene in the lving room.
Regording tronsitions, semantically coherent scane transtormations. and changes in camera angles are alowed. total number of words the description is around 70 words, shat count the ronge of 2-4, The theme the video (catagorat.
whispers acroas the barnen landscope. carrying with the scent of pina and cold air. the distance. herd wid relndeer moves slawly seross the plains. their movements graceful against the stark, frozen backdrop.
opens
Lmossperr
Transition
begins
S
not
vibi
then
Shat Count:
Shot Count:
n
view trees
wa
(h)
Figure 26: The details of the prompts used for evaluation. (a) Prompt for GPT-4o. The designated video theme vary. (b) Prompt examples for evaluation. (c) Word cloud of prompts for evaluation.
A Generated Video
Inter-shol Consisnlency
Consistency
Intru-shot Consistency
Reference Sel (flmeediedd)
Shot Segment
Medbl boaaatic-bsaal
Same pipeline
Middd
distribttion
enboldding Similarity
embbedding Similarity
prompt) Specified Shoc Count:
Genernted
For videos.
Divergence
For videos.
Traneiioon Cuntrol Stane
distibutiio
Average
Average
(Coniiiency
Gap (Cmis Cap tiiieeg
Scare
0.4
Aesthetic Quality
Somantic Consistency
0.0
A wontn with curly hair and green eyes stright bluz
Sgemested
Sppscified
Score
X
(From prompt) text
Transition Control
Overall Quality
Figure 27: Overview of the metric design. We devise evaluation measures along three complemen- tary dimensions: transition control, temporal consistency, and overall video quality.
31

---

Published as a conference paper at ICLR 2026
Table 12: Quantitative results for Overlapping Ambiguous Shot Boundaries, The best in bold. Smaller i values approach the fixed transition points.
Inter-shot Consistency
Method
Transition Control Score
Semantic
Visual
Scoret
Gapl
Scoret
Gap
Ablation for CineTrans DrT (training. free)
No Mask
0.206
0.5924
0,3421
0.5274
0.3574
== 2
0,269%8
0.7227
0.1918
0.8109
0.2612
0.4123
0.7344
0.1846
0.8044
0.2299
Fixed Transition Points
0.6864
0.7838
0.1772
0,7844
0.1943
Ablation for Cine'l CimeTrrans-Dii (trained)
No Mask
0.2112
0.6532
0,3422
0.6087
0.3312
0.7526
0.3082
0.5461
0.7302
0,1837
0.8245
0.2764
Fixed Transition Points
0.7003
0.7858
0.1552
0.7874
0.1901
Table 13: Details of prompt categories for evaluation.
category
count
scenary
34
architecture
10
human
25
object
31
Transition Control. For transition control, we define the Transition Control Score to measure whether the shot count the generated video aligns with the specified count, as formulated in Equation 5.
Figure 27 visualizes the calculation method for the Transition Control subpanel. In practice, when < 1, k is set to 2, and when æ 2 1, k is set to 1.6. Given that the prompts used for evaluation all specify multiple transitions, the score is set to when the generated video consists of a single shot. When the shot count in the generated video matches the specified value, the score is recorded as 1. More generally, the score is determined based on the absolute difference from the specified value.
Temporal consistency. In terms of temporal consistency, consider both intra-shot consistency and inter-shot consistency. Intra-shot consistency treats each shot as separate video and calculates the metric between adjacent frames using method similar to that in VBench (Huang et al., 2024). The focus of this section is on inter-shot consistency. As for frame extraction, we use the middle frame of each shot for calculation. However, if the video does not generate multiple shots, inter-shot consistency cannot be evaluated. As shown in Table 1, the original La (Wang et al., 2024b) lacks the ability to generate transitions, and therefore its inter-shot consistency metric does not have corresponding value.
Regarding the metric definition, inter-shot consistency cannot directly serve as the final evaluation metric. In multi-shot video generation, the goal is to ensure that the generated video aligns with the editing style of film-edited videos. High consistency would imply pixel-level similarity, which may contradict the multi-shot nature of real video editing. To address this, we extract 1000 film-edited videos as a validation dataset and compute their inter-shot consistency as a reference set. The final metric is then determined by the of the generated video and that of film-edited videos, as shown in Equation 6. A lower JSD indicates closer alignment with the reference distribution.
Distance (JSD) between the inter-shot consistency
Videos whose inter-shot consistency distribution aligns with film editing styles are considered to exhibit higher inter-shot consistency performance, while those deviating from film editing practices are regarded as having lower performance. This defines the evaluation metric based on inter-shot consistency.
32

---

Published as a conference paper at ICLR 2026
Figure 28: Failure case with similar composition consistency, which probably results from insuffi- cient training.
F LIMITATION
FAILURE CASE
The role of the mask mechanism in controlling the occurrence of transitions remains relatively sta- ble. Nevertheless, in occasional cases (particularly under the training-free setting), the compositions of different shots may become overly similar, illustrated in Figure 28. Although such content changes are perceptible to the human eye, the shot segmentation model does not recognize them as multi-shot videos due to the high compositional similarity, and the resulting visual experience also deviates from the film editing style. These cases can be attributed to insufficient training or the absence of such data types in the training set, which prevents the model from generating videos of the same scene with varied compositions. Potential improvements may come from expanding the dataset further training.
F.2 FUTURE WORK
Although CineTrrans has achieved promising results in cinematic multi-shot video generation ing the mask mechanism, there are still limitations to be addressed, along with several promising directions for future research.
While the mask mechanism enables control over the occurrence of shots, the specification of camera viewpoints could be made more controllable, for example, enabling changes in shooting perspective within scene that are more consistent with the film production pipeline. Therefore, achieving higher content consistency and more precise control over camera viewpoints will be key future direction, potentially requiring the incorporation of 3D information as prior knowledge.
Multi-shot videos could also be extended to greater lengths. Incorporating regressiee generation into the video generation pipeline represents promising approach for produc- ing longer multi-shot videos.
Fine-grainee consistency presents an area for further improvement. While our method demonstrates strong overall consistency, achieving one-grained alignment across shots re- mains challenging due to the intricate spatial understanding required. This limitation is primarily due the lack detailed background and scene context annotations in the train- ing data. Future work will focus on incorporating more granular annotations and leveraging advanced model designs address this challenge.
G USE OF LARGE LANGUAGE MODELS
We clarify the involvement of large language models (LLMMs) in the preparation of this work. LLMs not used for research design, methodological decisions, experimental analysis. Their use is limited to two aspects: (i) improving the clarity and readability of the manuscript through language editing, and (ii) generating evaluation prompts during the assessment phase, explicitly noted in Section 5.2. All substantive research contributions, including the conception of the problem, model design, implementation, and analysis, conducted entirely by the authors.
33


Reviews:
[{"review_id": "3BD4igj1nn", "reviewer": "Reviewer_6pc7", "summary": "The paper proposes CineTrans, a masked-attention mechanism for controllable multi-shot, film-style transitions in video diffusion models, together with Cine250K, a curated multi-shot dataset. A simple block-diagonal mask added to attention logits enforces strong intra-shot and weak inter-shot correlations, aligning with observed attention patterns; the method works training-free and improves further with fine-tuning, achieving strong transition control and competitive quality on comprehensive metrics.", "questions": "1. Layer selection sensitivity: Appendix E.2 suggests the best results with late layers for UNet and middle layers for DiT. Can you provide a brief heuristic or automated criterion for selecting mask layers on a new backbone, and quantify robustness to layer shifts?\n\n2. Boundary robustness: At inference, how sensitive is control to small timestamp jitter in the provided shot boundaries, and do you recommend padding a few frames around boundaries to avoid failure cases like Figure 20?\n\n3. Metrics specification: Appendix F mentions exact definitions. For completeness, would it be possible to include the precise formula for Transition Control Score and Consistency Gap in the main text or a boxed definition?", "limitations": "", "rating": 10, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- Clear, well-motivated mechanism: The block-diagonal mask is explicitly defined and integrated into the attention logits, with principled alignment to measured intra- vs inter-shot attention structure.\n\n- Strong empirical gains in control: Transition Control Score improves markedly over large T2V and multi-shot baselines while preserving quality.\n\n- Thoughtful evaluation design: The paper evaluates transition control, intra-/inter-shot consistency, and aesthetic quality, including a novel Consistency Gap aligned to a film-edited reference set.\n\n- Ablations that justify design: Impact of masking different layer ranges is analyzed for both UNet and DiT architectures, guiding the chosen masking strategy.\n\n- **High-quality supplementary material: Appendix is detailed, code and logs are included, and an HTML project page with videos aids understanding and reproducibility.** Kudos on the hard work!", "weaknesses": "- Over-hard masking; missed opportunity for temporal scheduling: Equation (2) uses a binary mask with 0 on same-shot pairs and $-\\infty$ across shots, which hard-zeros inter-shot attention in Equation (3). While effective, this may induce abrupt changes (also visible in the shared videos). A time-dependent or diffusion-step-dependent penalty could yield smoother transitions, e.g., replacing $-\\infty$ by $-\\alpha(t)$ that reaches $-\\infty$ for a couple of time steps, this ramps near shot boundaries and across denoising steps, then saturates, before relaxing post-transition. This would preserve some cross-shot context pre-cut and reduce artifacts at boundaries. Please consider and, if possible, report a small ablation of annealed masks; if not possible, then consider it as a suggestion for future work.\n\n- Related work gap: Prior work on long video generation with temporal control, e.g., VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis [ICLR’25], is not discussed. Please position CineTrans relative to these temporal-control strategies and clarify the conceptual differences between mask-based control and these schedules. Additionally, multiple prior works have considered Masked Attention for various applications (not just video generation). It would be a good idea to discuss a few of these related works as well, briefly.  \n\nReferences:\n\n[ICLR25] Li, Yumeng, et al. \"VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis.\" The Thirteenth International Conference on Learning Representations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jzctd9883q", "reviewer": "Reviewer_ZvZp", "summary": "This paper proposes a novel framework for generating coherent multi-shot videos with film-style transitions by introducing a block-diagonal attention mask and a “Visible-First-Frame” mechanism in video diffusion models, enabling precise shot boundaries and stable temporal consistency. Additionally, this paper constructs a 250K video–text dataset with frame-level shot labels to support video diffusion models in generating cinematic transitions and maintaining inter-shot consistency. This paper proposes a series of comprehensive metrics to evaluate the results of multi-shot video generation.", "questions": "1. Using the mask strategy requires knowing between which frames a shot transition occurs. How does this paper determine the frame length of different shots when generating multi-shot videos? Is it manually set, or controlled by hyperparameters? If it is manually set, how do you ensure that a complete action can be generated within a single shot without being interrupted?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. This paper contributes a large multi-shot dataset of 250K videos with frame-level shot boundaries and hierarchical captions.\n2. The proposed method is simple and easy to follow.\n3. The paper is well-structured and easy to read.", "weaknesses": "1. The method in this paper only adds a mask mechanism between shots to ensure content consistency, but this makes it difficult to maintain fine-grained consistency across different shots, especially for background regions, as shown on the left side of Figure 5. Table 1 (Intra-shot Consistency) also demonstrates that the improvement in consistency achieved by this method is limited compared to the baseline.\n2. Although the paper claims to achieve cinematic transitions, the proposed method only supports hard cuts, which limits its range of applications.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9uQMDkvXHz", "reviewer": "Reviewer_wQCP", "summary": "The paper introduces CineTrans, a diffusion-based framework for generating coherent multi-shot videos with cinematic transitions, aiming to bridge the gap between single-shot and multi-shot video generation prevalent in recent works. To support this, the authors construct Cine250K, a large-scale, multi-shot video-text dataset with detailed shot annotations and hierarchical textual descriptions. CineTrans leverages an observed block-diagonal attention pattern for shot boundaries in diffusion models, and implements a mask-based control mechanism that enables frame-level control over cinematic transitions—even in a training-free regime. The approach is empirically validated against several strong baselines, showing superior transition control, temporal consistency, and overall quality via both standard and custom metrics.", "questions": "1. Robustness to Ambiguous or Overlapping Shot Boundaries: How does CineTrans perform when shot boundaries are ambiguous or overlap semantically/visually (e.g., with crossfade-like transitions)? Is the mask mechanism brittle or robust under these settings?\n2. Mask Relaxation Variants: Have the authors experimented with soft masks or probabilistic boundary definitions? Are there gains in aesthetic quality, or does this impair transition control?\n3. Aesthetic Quality Decline After Fine-Tuning: What is the hypothesized cause of the decline in Aesthetic Quality (Table 2) after fine-tuning? Is it dataset-related or architectural?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The work introduces a novel mechanism that discovers and leverages a block-diagonal pattern in attention maps for transition control, directly inspiring an improved architecture; it further uses a mask-based strategy to align model internals with multi-shot video structure, enabling precise, shot-wise editing. A new large-scale dataset, Cine250K, fills a key gap with rich annotations (including frame-level labels and semantic stitching) and follows film-editing conventions. Extensive experiments provide thorough ablations and comparisons on transition control, intra- and inter-shot consistency, and aesthetic quality, complemented by insightful visual analyses. The approach transfers cleanly to customized or pre-trained diffusion backbones and improves both transition control and consistency in training-free and fine-tuned variants, with clear, intuitive examples and visual ablations highlighting advantages over existing methods.", "weaknesses": "1. Lack of Analysis on Limiting Scenarios: The paper demonstrates strong results on curated prompts and the Cine250K distribution, but does not critically examine or quantify limitations outside this scope, e.g., severe domain shifts, failure cases, or fundamental breakdowns of mask-based control when transition points are ambiguous or overlap.\n2. Limited Theoretical Rigor or Insights: While the empirical demonstration of attention map patterns is clear (see Figure 4), the work lacks theoretical analysis or ablation to clarify why these attention correlations emerge (e.g., role of architecture, dataset, or objective). Are these properties universal, or dataset/model-dependent? This affects reproducibility and generalizability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)