Captain Cinema: Towards Short Movie Generation
Authors: Junfei Xiao
Ceyuan Yang
Lvmin Zhang
Shengqu Cai
Yang Zhao
Yuwei Guo
Gordon Wetzstein
Maneesh Agrawala
Alan Yuille
Lu Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Video Generation
Diffusion Transformer
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present a method  for short movie generation covering data, system design and evaluation.

Abstract: We present **Captain Cinema**, a generation framework for short movie generation.
Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for a video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step is referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we introduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a curated cinematic dataset consisting of interleaved samples for video generation. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narratively consistent short films.

Published as a conference paper at ICLR 2026
CAPTAIN CINEMA:
TOWARDS SHORT MOVIE GENERATION
Junfei Xiao 1.2
Ceyuan Yang?*
Lymin Zhang²
Shengqu Cai2.3 Yang Zhao?
Yuwei Guo
Gordon Wetzstein
Maneesh Agrawala Alan Yuille' Lu Jiang?
Johns Hopkins University
*ByteDance Seed
Stanford University *CUHK
ABSTRACT
We present Captain Cinema, a generation framework for short movie genera- tion. Given a detailed textual description of a movie storyline, our approach firstly generates a sequence of keyframes that outline the entire narrative, which ensures long-range coherence in both the storyline and visual appearance (e.g., scenes and characters). We refer to this step as top-down keyframe planning. These keyframes then serve as conditioning signals for video synthesis model, which supports long context learning, to produce the spatio-temporal dynamics between them. This step referred to as bottom-up video synthesis. To support stable and efficient generation of multi-scene long narrative cinematic works, we in- troduce an interleaved training strategy for Multimodal Diffusion Transformers (MM-DiT), specifically adapted for long-context video data. Our model is trained on a curated cinematic dataset consisting of interlcaved samples for video genera- tion. Our experiments demonstrate that Captain Cinema performs favorably in the automated creation of visually coherent and narratively consistent short films.
INTRODUCTION
Narrative is central to how humans communicate, remember, and perceive the world. As Bruner has argued [Bruner 1991), narrative structures are fundamental to organizing human experience, while
Harari (Harari 2014) emphasizes that shared stories underpin the development of societies. In the
field of video generation (Ho et al. 2022b (iinger al. [0222] Chen et al., 2023 2024b Ho et al. 2022a Wang et al.) 2023 Zhou et al. 2022). recent advances, especially in diffusion based (Peebles)
& Xie 2023 Brooks et al. 2024| Hong al. 2022 Yang al. 2024c) and auto-regressive mod-
els (Kondratyuk et al. 2024 Sun et al. 2023b
2024 Wang et al. (024b) have enabled impressive
progress synthesizing short video clips for tasks such as image animation (Chen al., 2025 Xu ct al. 2024), video editing (Feng et al. 20244, Ceylan et al. 2023). and stylization (Huang et al., 2024b). However, these accomplishments predominantly address visual fidelity and local temporal coherence. leaving the deeper challenge of producing videos that convey coherent, engaging nar- ratives over extended durations. Bridging this gap from visually plausible snippets to full-length, story-driven videos-constitute
pivotal frontier for research and practice.
Automating the generation of feature-length cinematic narratives remains underexplored. Achieving narrative coherence and visual consistency over extended durations demands models that capture long-range dependencies while retaining fine-grained detail. Existing approaches frequently en- counter exploding context lengths, and visual drift when scaled to longer videos. To overcome these challenges, we propose Captain Cinema, a framework tailored for story-driven movie synthesis.
Captain Cinema balances global plot structure with local visual fidelity through two comple- mentary modules. A top-down planner first produces a sequence of key narrative frames that outline the storyboard, ensuring coherent high-level guidance. A bottom-up video synthesizer then interpo- lates full motion conditioned on these keyframes, maintaining both narrative flow and visual detail. Central to this design is Go denMem, memory mechanism that selectively retains and compresses contextual information from past keyframes. By summarizing long histories without exceeding memory budgets, Gol deldemmem preserves character and scene consistency across multiple scenes with super-long long context, enabling scalable generation of multi-scene videos.
"Corresponding author.

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Published as a conference paper at ICLR 2026
LCT /
Captain
Sora / Kling/ Veo2...
VideoAuteur
Cinema
Shot-level
Scene-level
Movie-level
Single
shot
88
Multiple shots
60s
Multiple
scenes
CAPTAIN
CINEMA
1,000
Figure 1: Captain Cinema: "I film this all day!" Captain Cinema bridges top-down in- terleaved keyframe planning with bottom-up interleaved-condiiionng video generation, taking a step toward the first multi-scene, whole-movie generation, preserving superior visual consistency in scenes and identities across the long video. All the movie frames are generated.
Additionally, we build a specialized data processing pipeline for processing long video data for movie generation and introduce progressive long-context tuning strategies tailored for Multimodal Diffusion Transformers (MM-DiT). These techniques enable stable and efficient fine-tuning on large-scale, long-form cinematic datasets, addressing the challenges of multi-scene video genera- tion. Extensive experiments and ablation studies demonstrate that Captain Cinema not only achieves strong performance in long-form narrative video synthesis but also enables the automated creation of visually consistent short films that significantly exceed the duration of existing works, setting milestone in multimodal video generation capabilities.
RELATED WORKS
Text-to- Video Generation. Text-to-vide video models (Ho et al.
2022b; Singer et al. 2022 Chen et al.
2023 2024b; Ho al., 2022a; Wang et 2023) Zhou al. (002) now generate 5-10 clips with high visual fidelity. Most adopt latent diffusion paradigm Rombach et al. (022); diffusion variants such as DiT (Peebles Xie 2023), Sora (Brooks et al. 2024). and Cog Video (Hong et al. 2022 Yang et al. 2024c) extend this design with larger datasets and refined denoisers. Autoregressive alternatives like VideoPoet (Kondratyuk et al.) 2024) and the Emu family (Sun et al., 2023b) 2024 Wang et al.) 2024bb instead predict discrete visual tokens sequentially. These approaches remain clip-centric and lack mechanisms for narrative or visual consistency over longer horizons. We bridge this gap by introducing explicit consistency regularisation for full-length movie generation.
Interleaved Image-Text Modeling & Conditioning. Interleaved image text generation (Dong ctt al. 2023 Tian et al., 2024a / Ge et al. 2024 Yang et al. 2024a; Alayrac et al. 2022) has emerged
as promising paradigm ffor producing richly grounded multimodal content. Early efforts (Radford Li et al. 2023b Sun et al. 2023ab leveraged large-scale image-text corpora (Gadre
et al. al.
2021
2024 Schuhmann et al. 2022) but were largely restricted to single-turn tasks such cap-
tioning or text-to-image synthesis. The advent large language models (Touvron et al.
2023) and
unified vision-language architectures (Li et al. 2023a
Liu et al. 2024
Wang al.
2024a has
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enabled more sophisticated interleaved reasoning, yet most existing systems generate content only once and overlook coherence across multiple shots. VideoAuteur (Xiao et al.
2024) addresses this problem through interleaved VLM director and LCT (Guo et al. 2025) directly finetunes MM- DiT' to peform long interleaved video generation. Although Diffusion Forcing (Chen et al., 2024a), LTTX-Video (HaCohen et al. 2024a) and Pusa (Liu & Liu 2025) support multi-keyframe condition- ing, they remain confined to single-shot scenarios. By contrast, our approach addresses multi-shot interleaved conditioning, explicitly modeling cross-shot coherence for long-form video generation.
Narrative Visual Generation. Existing research narrative visual generation primarily focuses on ensuring semantic alignment and visual consistency. Recent methods VideoDirectorGPT (Lin
et al.
2024), Vlogger (Zhuang et al., (0024). Animate-a-Story (He et al., (023), VideoTetrii Tan
et al. 2024b), IC-LoRA (Huang et al.] (2024)), and StoryDiffusion (Zhou et al., (0024) pursue
these goals through diverse architectural and training strategies. While most prior work concen-
trates on producing semantically consistent image sets (Huang et al.) 2024a Yang et al. 2024b
Zhou al. 2024), our objective to generate fully coherent narrative videos. Although certain approaches condition synthesis on text (Zhuang et al. 2024] Xu et al. 2024) or augment prompts with keyframes (Zhao al., 2024), our framework explicitly disentangles long-form generation into (i) interleaved keyframe synthesis and (ii) multi-frame condie-oodiiooeedd video completion. This design achieves state-of-the-ar results long narrative movie generation, offering superior visual fidelity, temporal coherence, and robustness to super-long context conditions.
Visual Generation with Token Compression. The computational demands associated with syn- thesizing high-resolution visual content for video sequences necessitate efficient data representation strategies. FramePack (Zhang & Agrawala (025) employs context packing in video generation to compress input frames, thereby maintaining fixed context length for enhanced processing effi ciency. McC (Cai et al.] 2026) instead trains a learnable dynamic sparse attention router to select relevant contexts at the attention level. FlexTok (Bachmann et al. 2025) utilizes an adaptive ID tokenizer for images, adjusting token sequence length according to image complexity to achieve mantic compression. LTXVideo (HaCohen et al.) 2024b) uses compressed VAEs to improve model efficiency. PyramidFlow (Jin et | (0024) performs video diffusion in a pyramid and renoises the multi-level latents. FAR (Gu et al. (025) proposes a multi-level causal history structure to establish long-short-term causal memory and discuss KV cache integrations. HiTVideo (Zhou et al., 2025) uses hierarchical tokenizers video generation models and connect to autoregressive language mod- els. Memory in discrete space like keyframes is less discussed but plays an important role in our framework to process frame contexts, optimized for synthesizing long-form narrative visuals.
METHOD
3.1 LEARNING LONG-RANGE CONTEXT IN MOVIES
Different from (Guo et al., 2025 Huang et al., 2024a) trained on single-scene data, we directly leam the movic generation task from entire movies. Specifically, our keyframe generation model is trained on interlcaved image-text pairs, while our vidco generation model learns from interleaved video-text pairs, significantly enhancing its generalization across scenes and cinematic contexts.
Data. We collect data from public available sources as our experimental dataset, amounting to total length of approximately 500 hours. We process whole movies with the following data pipeline to produce interleaved keyframe-text pairs as well as shot-text pairs. After processing these data, we obtained roughly 300,000 keyframes and video shots train our top-down interleaved keyframe generation model ($32) and the bottom-up interleaved conditioned video generation model 33.
Processing Filtering. We detect scene cuts with PySceneDetect (Breakthrough) 2021) I and ex- tract mid-clip frame from each resulting segment. After removing black borders, we center-crop to a 2:1 aspect ratio and resize so the shorter side is 400 pixels. We then run Gemini-2.0 Flash (Gem-) uninformative frames and to generate detailed captions
ini Team, 2023) to discard low-quality for the remaining keyframes. To annotate characters, we include consistent <cha racter name> tags preserve IPs. Section dettaals the prompt used for keyframe annotation.
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Scene 1
Scene
Scene
A medium shot captures <Harry Potter> levitating cake woman's head, hands outstretched in concentration. A left, obiivious. The setting cluttered dining with striped wellpeper nd decoretive lamps. The atmosphera tense magical, blending the mundane with the extraordinary.
The shot, framed by barred window, features three characters. <Ron Weasley? is in the front, smiling slightly, while another ginger-haired character slightly behind him the left. Another character's face is partially vieible behind and to the light subdued, creating mysterious atmosphere. The setting appears to be inside vahicle.
High-angle shot averlooking <Durslays>, <Vernon Dursleys trantically locks a door with multiple padlocks.
man
<Petunie Dursley> held <Dudley Dursleyz striped pajames, The aetting appeers to be cremped hallway the <Duraleys>'
right of Ron.
Illuminated with herah lighting. The atmosphere is frantic and feartul.
Learn from whole movies. Here is an interleaved data sample processed from full-length movies. Our data pipeline extract structured narrative and visual information across scenes. Each frame is annotated with detailed visual description with major <Character Names>> of movies.
Figure 2:
3.2 TOP-DowN INTERLEAVED KEYFRAME PLANNING
In this section, we introduce our method to finetune pretrained Text-to-Image Model (i.e., Flux 1.Dev (Labs 2024)) for interleaved keyframe generation with stability and efficiency.
Hybrid Attention Masking with MM-DiT. Our design builds on Flux, whose model is split into D double-stream (image-text text) blocks followed by S single-stream blocks. Given P imagee text pairs ((x,, 1=)) with x; RLxxd and y/s E TR xd concatenate the tokens z; [a>]|ysh]
Local (double-stream) blocks. Each of the first blocks uses a block diagonal mask Mocal such that 2, attends only to itself.
2. Global (single-stream) blocks. Their outputs are concatenated Z |all.... [| and passed to the next blocks. A full mask Mgtobal = enables bi-directional inter-pair attention during training; swapping it for an upper-triangular mask yields causal, auto-regressive generation.
The types masking (cf., Fig. 3b) keeps early computation local and efficient while later blocks aggregate global context, achieving coherent interleaved key-frame generation.
GoldenMem:
Compress Long-Context Visual Memory. As we target movie generation, how to design a long-context memory bank of generated visual frames is challenge when the context growth. Inspired by the golden ratio squares with an upper bound area, we propose GoldenMem to compress long context visual frames through golden ratio downsamling with semantic-oriented context selection. To maintain long visual context without inflating the token budget, we store only the current frame full resolution and encode earlier frames at progressively coarser scales. Let the golden ratio be y (I + v 2 se 1.618 and denote the short side of the newest latent by (c.g., 8o 25). We downsample the i-th previous frame by
S; = |1-1/4],
i= 1,
which yields the partitioned into overlalalgp p p patches, contributing t; (s:/p)² memory tokens. Because decays geometrically, the total conditioning cost
sequence (25, 15. 9. when ho = 4. Each x latent is
Σ 4 io(1 -
to 1.62
remains a constant factor above the single-frame cost tn. Thus GoldenMem preserves a (k+1))frame history with fixed small overhead, visualized in Figs. and
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Less tokens but more memory context
10
MM-Diffusion Transformer Blocks [Local Attention)
55
MM-Diffussio Transformer (Global Attention)
Inverse Fibonacei
Downsampling
(c) GoldenMem: Visual Context Compression
Image
Multi-keyframe Conditioned HM-Diffusion Transformer (Global Attention)
Image
Pakc
Image
02
PD
13
Imre polr Attontion
Attantion
Inter-pur Attentionn (Causa))
(BI-drrcctionn))
[Bichmectooaa
(a) Bridging Top-down & Bottom-up Interleaved Generation
(b) Hybrid Attention Masking
Figure 3: Method Overview. Captain Cinema bridges top-down and bottom-up interleaved for one-stage multi-scene movie generation. introduces a hybrid attention masking strategy with GoldenMem context compression to learn and generate long movies efficiently and effectively. The number GoldenMem tokens (referring to the short side encoded image latents) an example to show the inverse Fibonacci downsampling operation for long context compression.
Naive GoldenMem
Coverage Rate (%)
ClipT7i
48.2%
40000
33072
40-
T5_T2T
39.4%
27560
22048
30-
26.1%
20000
11024
16536
16.7%
10000
10-
8984
9696
10804
Sample Num
Figure 4: GoldenMem compresses the the context length. The x-axis shows the number image-text pairs, and the y-axis is the total number of tokens. Ini- tial resolution is 400x800 (H.
Figure 5: Semantic-oriented context retrieval. CLIP text-to-image beats T5 text-to-text for history context retrieval for in-context storyline video extension.
Semantic-Orienter Context Conditioning Beyond Temporal Distance. Although sequential gen- eration is intuitive, films frequently employ non-linear devices such as flashbacks, foreshadowing, and temporal loops. We therefore retrieve context by semantic relevance, conditioning each new frame on embeddings obtained with CLIP (text-image) and T5 (text-text). This strategy accom- modates complex narrative structures more effectively than striet temporal conditioning. shown in Fig. 5 CLIP Text-to-Image retrieval consistently yields higher coverage than T5-Text-to-Text retrieval, leading to better memory recall for extracting most relevant history frames.
Progressively Finetuning with Growing Context Length. Directly finetuning on long context in- terleaved sequences are prone to model collapse and often generate messy backgrounds with broken semantics ($4.2.. To facilitate stable training over interleaved sequences, we use progressive train- ing strategy to finetune the model on interleaved data with growing context length. Specifically, progressively finetune the model from Linle-image genceration to 8, 16, and 32 interleaved pairs. Dynamic Stride Sampling for Interleaved Sequences. Limited by the data scale, naively sample consecutive interleaved keyframes can lead models get overfitted and be less robust. In this way, we use a dynamic stride sampling strategy to sample interleaved data, which provides thousands of times more valid data sequences (a bar of 25% overlap rate) than naive consecutive sampling.
3.3 BOTTOM-UL UP KEYFRAME-CONDITIONE VIDEO GENERATION
Our framework bridges two complementary viewpoints. From a bottom-up perspective, begin with a base video generator and expand its scope to long-form content by conditioning on a se- quence of interleaved shots. From top-down perspective, keyframes that act as visual anchors, then instruct the generator to fill the intervals between them. Unifying these perspectives allows to preserve local visual fidelity while maintaining global nar-
first construct sparse set of narrative
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AWrod
[Shod medium uhiaa stoweto
Jbhot misiam shat Bruse adhig
Shht 5) Jotor paring of winsstree
1Shot extitor the snoneatlp
[sht dooking of stodan
Alowwing slewport
[shor 17) unitin
jshot Aouw losk maboley
Shea ciurapp Rowr brt
Shot di Joase tober cntegnttel pome
Shof poseee Jon Sple-pp gase
Bhht 23) soe-angle shot of Appassh
[Shot 24] wide shat of spectotsior
23]
Figure 6: Qualitative results. From the narrative prompt " "An interstellar voyage with Bruce Wayne, the Joker, and Alfred Pennyworth," Gemini 2.5 Pro composes shot-level descriptions that guide top-down key-frame generator, yielding the storyboard panels shown above. Each text-key-frand pair then conditions bottom-up video model, which synthesises the full multi-scene film. The figure highlights twenty-four representative shots demonstrating sustained narrative coherence, char- acter fidelity, and visual style across the entire production. Video demo is in the supplementary.
rative coherence throughout the entire video. Given K keyframes {I ]x] each the first frame of its shot, we condition diffusion generator on (i) the tiled global caption Gated and (ii) the visual embeddings of all keyframes:
V = =TT, Gol GiKK Ciiled, l. = 2:Txk)
k k= K,
where G3 outputs frames fa. for shot k. Multi-key conditioning thus anchors appearance shot boundaries and enforces seamless motion dynamics across shots, generating videos that preserve narrative intent, visual details, and temporal coherence with strong visual consistency.
EXPERIMENT
Experimental Setup. We finetune Flux 1.Dev (Labs) 2024) interleaved movie dataset (de- scribed in $3.1 for interleaved keyframe generation. All keyframe models are trained for 40,000 steps with batch size of 32 using 32 H100 GPUs. For interleaved video generation, we adopt Seaweed-3B (Seawead et al., 2025 Guo et al., 2025) as the base model and finetume it with multi- frame interleaved conditioning for 15,000 steps using 256 H100 GPUs. Additional implementation details are provided in the appendix. The keyfram feyframes are cropped with max area to 400x800 resolu- tion without aspect ratio changes while the video generation data processing resizes all the videos an approximation area of 230,040 total pixels. More implementation details of training our bottom- up and top-down keyframe and video generation models are provided in Section
4.1 MAIN RESULTS
Qualitative results. Fig. 6 shows qualitative result with storyline generated by Gemini 2.5. Specifically, we first prompt Gemini to generate a sequence long-form captions describing a storyline inspired by Bruce Wayne, Alfred Pennyworth, and Joker, and interstellar travel. We then

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Published as a conference paper at ICLR 2026
Warmup
Warmup (Step 80K)
Moderate Warmup (Step 40K)
Figure 7: Progressive long-context finetuning needs modest warmup. As the base model (FLUX
1.Dev) is distillation model with guidance, it requires progressive finetuning with growing context length to warmup the model. However, over warmup can also lead the model to forget the distilled knowledge for high-quality keyframe generation, generating artifacts messy textures. Picking proper warm-up length is therefore important for long context tuning.
(a) Multi-frame Finctuning Makes Artifacts
(b) Context-aware "World" Generation
(c) Smooth Camera Movement
Figure 8: Disentangled task modeling makes robust movie generation. With the high-qualit keyframes generated by our top-down keyframe generation model, the bottom-up video generation model can now focus on motion dynamics. The generation performs higher temporal consistency, world-context awareness and smoother camera movement.
use our interleaved top-down keyframe generation model to generate shot-wise keyframes based on the narrative. Finally, construct interleaved text-keyframe pairs from the generated content and use them as conditioning input for the LCT generation model to produce the final movie. The results demonstrates compelling qualitative performance in the aspects of visual quality, consistency and semantic alignment with prompts. We also provide additional qualitative results of keyfra keyframe generation in Section and also multi-scene movie results in the supplementary material.
Method
Visual
Temporal
Semantic Textt
User Study
Aesthetict Quality Consistency? Dynamic
Quality Semantict
IC-LoRA (Huang et al. 2024a
54.1
60.5
88.7
61.1
23.1
1.5
2.3
LCT (Guo et al. 2025)
56.2
59.9
94.8*
51.8
23.9
2.4
Ours (w/o MF-FT)
56.8
91.9
64.4
25.7
2.8
3.5
Ours
57.2
61.7
91.0
65.4
26.1
3.3
3.7
Table Quantitative Evaluation User Study, We employ automatic metrics and average human ranking (AHR). "Consistency" represents the average score of subject and background consistency. Most video clips show low temporal dynamic but perform high TC score (Zheng al. 2025).
Quantitative Evaluation User Study. We follow VBench-2.0 (Zheng et 2025 eval- uate visual and temporal aspects, and follow the LCT (Guo et al.) 2025) protocol for assessing sext-semantic alignment. A user study is conducted using a 4-point scale Very Good, Good, Poor, and Very Poor frocusfo
semantic alignment and overall visual quality. As few works target long-video generation, we primarily compare against two closely related baselines: LCT and IC- LoRA (Huang et al. 2024a) combined with 12V (Seawead al.) (025). To ensure a fair comparison, we use GPT-4o to format consistent scene prompts and generate videos across all methods. Tab. [ shows that approach performs favorably across most metrics, finding corroborated by user studies evaluating video quality and semantic relevance. Notably, our advantages are most evident for generating coherent and vivid motion over long sequences in cinematic content creation.
Long-context stress test. We evaluate robustness the context window grows from 8-+48 in- terleaved pairs. For each setting we generate 20 clips and ask Gemini Flash 2.5 to score Consis- tency, Quality, Diversiry, and Narrative Coherence; and another Identity Consistency metric used in VBench-2.0. As context length increases, LCT quality degrades sharply (Tab, 2), whereas ours re- tains 93% of its consistency even at 48 pairs, validating the effectiveness of GoldenMem (Fig.

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Consistency
Visual Quality Aesthetic
Diversity Diversity
Narrative Nar. Coher.
Identity Identity
Method
Ctx. Pairs
Character
Scene
Visual
TCT
(Guo al.
2025
8
4.3
3.5
4.8
4.1
3.0
2.8
0.43
LCT
Guo
2025
16
3.6
3.4
4.5
3.9
2.5
2.8
0.31
L.TT
(Guo al.
2025
3.1
0.7
1.7
0.6
Ours
8
4.9
3.9
4.9
4.7
4.5
4.0
0.51
Ours
16
4.8
3.8
4.9
4.6
4.2
3.8
0.47
Ours
4.6
3.8
4.2
3.6
Ours
32
4.5
3.8
4.9
4.6
3.9
3.4
0.37
Ours (w/ G.Mem)
4.6
3.8
4.6
4.2
3.8
0..4
Ours (w/ G.Mem)
4.6
3.5
4.0
3.5
0.35
Ours (w/ G.Mem)
48
4.5
3.0
4.7
4.5
3,9
3.3
0.31
Long-Context Stress Test. We exhibit long context stress test benchmark the robustness of long context generation. We use Gemini 2.5 Flash to rate the generation quality in multiple aspects (detailed in Section HH). and automatic identity consistency metric introduced in VBench
Table 2:
2.0. Our interleaved method with GoldenMem can do high-quality long context generation with strong consistency preservation of both characters and scenes.
GoldenMem
Compute PFLOPS (Est.
Visual
Text Alignmentt
# Pairs
# Groups
Images
Qualityt
Consistencyf
16 16-frame
30
4.4
4.7
4.1
16-frame
3
21
4.3
4.3
4.1
12-frame
55
4.1
4.3
3.8
92-frame
3
35
3.9
4.2
3.6
48-frame
OOM
48-fram 38-frame
3 12
52
3.6
4.2
3.5
Table GoldenMem makes longer context with less computation. The first row of each block is the baseline while the second row is using GoldenMem. GoldenMem uses less computation with performance preservation and processes longer context window without out-of-memory (OOM). Compute is estimated through #tokens steps FF.OP O////t/ttke........
4.2 ABLATION STUDIES
Progressive Long-context Finetuning. As detailed in $3.2 we employ progressive long-context fine-tuning, gradually expanding the context window the interleaved keyframe generator. Fig. 7 shows that directly fine-tuning the FLUX base model leads to training collapse. On the other hand, fine-tuning from a late checkpoint (step 80,000; half the target context length) also produces visual artifacts, which likely caused by the knowledge forgetting for the FLUX distillation base model. So, progressive finetuning with FLUX-like distillation models needs modest warmup (i.e., step 40,000) avoid training collapse forgetting distilled guidance.
From Short to Long:
Compressed Long-Context Memory. As detailed in $3.2, we downsample his- torical visual context at the pixel level in inverse Fibonacci sequence, thereby capping the overall number of visual tokens. Tab. aablatts this design with different number of interleaved pairs. Gold- enMem demonstrates strong compute efficiency under the same visual context, performing minor visual quality and consistency degradation. Moreover, GoldenMem enables longer context window (i.e., from 32 to 48 without 0OM), which keeps longer context history for long video generation.
GoldenMem:
Robust Long Video Generation with Disentangled Task Modeling. As detailed in $3. fine- tune pretrained MM-DiT video generator (Seawead et al. (025) with multi-fram frame interleaved con- ditioning serve our bottom-up component. Coupled with the top-down interleaved keyframe generator, this design improves robustness, yielding fewer artifacts (Fig. (Fig.B8h), consistent environment dynamics e.g., burning ruins with rising smoke (Fig. (8)) and smooth camera motion with sta- ble character identities (Fig. (D). These results demonstrate that disentangling high-level narrative planning from low-level motion synthesis enables efficient and robust long-video generation.
Dynamic Strided Data Sampling. Because the scale of movie dataset is relative small to the data used for base-model pre-training, naive sampling induces rapid overfitting and weak gen- eralization, particularly under sparse keyframe conditioning and limited interleaved samples. We
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Condition Noise Scale
Figure 9: Visual context conditioning needs moderate noise injection. Each conditioning frame has individually been injected with random noise at varying levels: Ist: 751~1000; 2nd: 501~750; 3rd: 2-1~500; 4th: 1-2 Context conditioning with moderate noise lead to better consistency.
(a) Creative scene generation. Batman locked prison while Joker takes control of Gotham.
(b) Cross-movie character swapping. Bruce Wayne and Alfred Pennyworth appear "Interstellar." Figure 10: Captain Cinema generalization abilities. Trained on high-quality, richly annotated interleaved data, our model offers creative control at the movie, scene, and character levels, allowing limitless "parallel universes" that maintain coherent narratives and high visual fidelity.
mitigate this by introducing dynamic strided sampling scheme that systematically offsets the sam- pling stride across epochs. This strategy yields 100 times more valid data when sample 16-frame sequence from 32 frames (25% keyframe-overlap threshold).
Noisy visual context conditioning. During our interleaved keyframes generation model training, each frame is applied with individual level of noise. So, we ablate the noise level for keyframe extending generation with given context of noisy latents. As shown in Fig. g our interleaved keyframe generation model fails to keep character consistency when conditioning on context with large noise injection (i.e. 501~ 1000 steps). However, our model shows very strong character consis- tency when using moderate noise level (i.e. 1~5500 steps) and shows best character facial perserva- tion when using small noise (i.e. adding 152 timesteps of noise) for long context conditioning. We also evaluate out-of-domain generalization to unseen characters in Section
4.3 GENERALIZATION ABILITIES
Creative Scene Generation. Our method could synthesize novel scenes that were never present in the training corpus by recombining familiar characters, settings, and plot elements. Leveraging semantic-oriented retrieval and interleaved conditioning, it produces imaginative environments, e.g., prison-bound Batman confronting the Gotham under Joker's control, while preserving visual fi- delity and narrative plausibility through one-stage generation. Our model effectively understands character traits, typical settings, and plausible interactions, enabling seamless integration of new creative ideas into cohesive, realistic scenes. Our model could extend any scene through context conditioning and continue story-telling with characters and scenes consistency.
Cross-Movie Character Swapping. Our pipeline also enables seamless swapping of characters across unrelated cinematic universes. Leveraging insert Bruce Wayne and Alfred Pennyworth into the science-fiction environment of Interstellar. This capability demonstrates a clear separation between character identity and scene context, providing a flexible platform for counterfactual narrative exploration.
embeddings, the system can

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5 CONCLUSION
We propose Captain Cinema, based on top-down interleaved keyframe planning with bottom-up, multi-keyframe-cndiiioo video synthesis to generate a short movie. Leveraging the Golden- Mem compressed visual context, progressive long-context fine-tuning, and dynamic strided sam- pling training strategies, our model sustains global narrative coherence while preserving local visual fidelity throughout feature-length videos. Captain Cinema also demonstrates generalization ability for creative scene generation and cross-mes movie character swapping. Despite the limitations discussed above, our work represents concrete step toward fully automated, story-driven movie generation.
ETHICS STATEMENT
Long-form video generation could democratize high-quality animation, documentary creation, ed- ucation, and simulation, enabling content production that previously demanded extensive budgets and expertise. This technology also provides valuable tools for individuals with limited mobility or resources and offers new avenues for reinforcement learning and robotics simulations. How- ever, accessible video generation also raises concerns around hyper-realistic misinformation, non- consensual media, intellectual-propent tational costs. To mitigate these risks, we will adopt gated model releases, implement robust wa- termarking, provide model documentation, and enforce clear usage policies. Additionally, we will develop watermark verification tools, conduct external red-team audits, and collaborate with stake- holders on content detection benchmarks. All the data here are used for research only purpose.
violations, and environmental impact due to high compu-
REPRODUCIBILITY STATEMENT
We include all the key details including training hyperparameters, data collection and processing procedure, evaluation and annotation prompt etc.
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APPENDIX
Please visit the webpage in supplementary material for viewing a set of video demos and understand the model capabilities.
A IMPLEMENTATION DETAILS
We provide the training hyperparameters for the top-down keyframe generation model and the in- terleaved conditioned video generation model Tab. - The interleaved auto-regressive model is trained on interleaved image-text pairs with default resolution of 400 x 800, using batch size of 32 and 6 precision. It employs AdamW as the optimizer, with a peak learning rate of 2 x 10-4 and cosine decay schedule, training for 2,500 steps. Training context pairs vary between 2 and 8, while inference always uses 8 pairs for consistency. The visual-conditioned video genera- tion model processes video data at an area of 480x480 2230,400 total pixels), with a batch size of 64 and 6 precision. It uses AdamW with peak learning rate of 1 x 10-5 and constant decay schedule, training for 20,000 steps to handle temporal conditioning effectively. We provide the train- ing hyperparameters for the top-down keyframe generation model and the interleaved conditioned video generation model in Tab. The interleaved auto-regressive model is trained on interleaved image-text pairs with default resolution of 400 800, using batch size of 32 and 6 preci- sion. It employs Adam as the optimizer, with a peak learning rate x 10-4 and a cosine decay schedule, training for 2,500 steps. Training context pairs vary between 2 and 8, while inference always uses 8 pairs for consistency. The visual-conditioned video generation model processes video data at an area of 480x480 ( 230,400 total pixels), with a batch size of 64 and 6 precision. It uses Adam W with a peak learning rate of 1 x 10 and a constant decay schedule, training for 20,000 steps to handle temporal conditioning effectively.
Configuration
Keyframe Generation Model
Video Generation Model (180P, native AR) x 7
Resolution
400 x 800 x T.
Optimizer
AdamW
AdamW
Optimizer hyperparameters
3: 0.9, _32 -2.999 1-10-
B1 0.9,
82-0.95, 6-10-8 1 x 10-4
Peak learning rate
4 10-4
Learning rate schedule
Linear warm-up, cosine decay 1.0
Linear warm-up, cosine decay 1.0
Gradient clip
Training steps
20,000 (per stage) 500 16
10,000 (total) 1,000 64
Warm-up steps
Batch size
Numerical precision
bfloat16
bfloat16
Computation
32 x H100, ~72
256 x H1100, 200
Table 4: Implementation details of our models. The left column lists the interleaved keyframe generator settings, and the right column lists the visual-conditioned video generator settings.
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B DATA ANNOTATION PROMPT
You are film-director assistant annotating single frame from the movie.
1. Detailed Caption
Describe visual composition, lighting, and camera angles.
Note character actions, expressions, and positioning.
Mention setting details and atmosphere.
2. Character Identification
Identify characters only when absolutely certain, enclosing names in angle brackets
(e.g. |Character Name ). Allowed list: ;Character Listi-
If uncertain, describe by appearance, role, or action-do not use angle brackets.
For dual-identity roles, choose the name that matches the on-screen persona.
3. Level of Detail
Craft concise yet thorough sentences capturing technical aspects (framing, lighting) and narrative elements (interactions, mood).
Focus specifies that make the frame distinctive.
C THEORETICAL ANALYSIS OF GOLDENMEM
GoldenMem can be interpreted as a budgeted multi-scale memory over the history of keyframes. Let the newest frame be encoded at short side sy and let the i-th previous frame stored at scale 8; = / where Φ = (1 /52 Up to integer rounding and the minimum latent size, the corresponding token count satisfies t; x too 22 because the latent is partitioned into fixed-size patches. naive memory that stores n frames at full resolution requires (n) = ntoo tokens, while GoldenMem requires
-22n
TeM(n) (
4
Σ
2 10
Φlp 1.618/o-
Hence, GoldenMem turns linear memory growth into constant-factor overhead over the most recent full-resolution frame. Since the dominant attention cost is quadratic in the number of conditioning tokens, the memory-dependent attention complexity drops from 0((nt) )) to (( The quali- tative implication is that GoldenMem allocates fidelity according to recency: recent frames preserve dense spatial detail, whereas older frames retain coarse but semantically useful anchors. Tab. 3 is consistent with this analysis, showing that the same design extends the context window from 32 to 48 frames without out-of-memorg while maintaining similar generation quality.
and identity
D FURTHER EXPLANATION ON CONTEXT SELECTION AND SEMANTIC RETRIEVAL
For each target keyframe with caption we build a candidate set from all previous memory entries {(z,, My }is and rank them by semantic relevance instead of pure temporal distance. The default score is CLIP text-to-image similarity.
rj simms (Seur (w:), (CL)
and the selected history is the highest-scoring subset that fits the GoldenMem budget. To avoid saturating the memory with redundant frames, we suppress obvious near-duplicates from the same shot and retain context that covers complementary visual states. This design addresses common ambiguity in long movies: captions repeatedly mention the same characters, camera types, or emo- tions, several earlier frames may be lexically compatible with the current caption when they belong to different scenes. Pure text-to-text retrieval can therefore over-match repeated wording and return semantically related but visually mismatched scene. By grounding the query against
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historical images. CLIP retrieval better disambiguates recurring characters and scene revisits while still allowing non-local context such as flashbacks or foreshadowing, When several candidates are nearly tied, only use recency a tie-brraker, which preserves local continuity without collapsing the retrieval to purely temporal heuristic.
E LONG CONTEXT STRESS TEST
This is to provide the additional charts for the long context stress test. We evaluate robustness as the context window grows from 8-+48 interleaved pairs. For each setting we generate 20 clips and ask Gemini Flash 2.5 to score Consistency, Quality, Diversity, and Narrative Coherence; and another Identity Consistency metric used in VBench-2.0. As context length increases, LCT quality degrades sharply (Tab. 2) whereas ours retains > 93% of its consistency even at 48 pairs, validating the effectiveness of Mem (Fig.
Consistency vs. Number of Context Pairs
Visual Quality Number of Context Pairs
4.5
Consistency Score
4.0
Visual Quality Score
4.0
3.5
3.5
3.0
3.0
2.5
Ours
Ours
2.0
Ours (W. GoidenMem)
Ours IW GoldenMem)
1.
16
24
Number Context Pairs
Number Context Pairs
(a) Consistency
(b) Visual Quality
Diversity Number Context Pairs
Narrative Number of Context Pairs
4.5
4.0
Diversity Score
4.0
Narrative Score
3.5
3.0
3.5
2.5
3.0
2.0
2.5
1.5
2.0
Ours
1.0
Ours
Ours (W. GoldenNam)
Ours (W. GoldenMem)
0.5
Number Context Pairs
Number Context
(c) Diversity
(d) Narrative Coherence
Figure 11: Long-Context Stress Test. While context length grows be very long, our method still maintains strong visual consistency, high visual quality, diversity, and narrative coherence even under extended context lengths.
F HYBRID ATTENTION MASKING IN TRAINING AND INFERENCE.
The local mask is fixed across training and inference: the first D double-stream blocks always a block-diagonal mask, so each image-text pair performs within-pair fusion before any cross-pair interaction. The distinction appears only in the subsequent single-stream blocks. During training. we use fully bidirectional global mask because the entire interleaved sequence is available and the objective is to learn symmetric long-range dependencies across all pairs. During inference, the model supports two regimes. For offline storyboard generation, where the captions of the whole sequence are given advance and frames are sampled jointly, we keep the same bidirectional global mask and perform liffusion-forcing style denoising; this is the setting used for the 32-frame qualitative examples in Appendix Sec. G For online extension or autoregressive movic continuation, replace the global mask with upper-triangular causal mask so that the current pair only attends to previously generated pairs and never to future ones. No causal masking is applied inside the local double-stream blocks because causality is relevant only to cross-pair dependencies, not to fusion within a fully observed pair. Thus, the architecture is unchanged between training and inference; only the global inter-pai pair mask is switched according to whether future pairs available.
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G ADDITIONAL QUALITATIVE RESULTS FOR KEYFRAME GENERATION. We provide non-cherry pick qualitative keyframe generation results in figures below.
Li the Julley
Weltoa'ee Shu Orgienengnnnnnn mAAD
Figure 12: Qualitative Result. Our method shows superior generation quality with character and scene consistency. 32 frames are generated by our method through diffusion forcing with bidirec- tional masking strategy. The prompt here is from the validation split with ChatGPT-40 re-imagined.
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Figure 13: Qualitative Result. Our method shows superior generation quality with character and scene consistency. 32 frames are generated by our method through diffusion forcing with bidirec- tional masking strategy. The prompt here is from the validation split with ChatGPT-40 re- re-imagined.
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Figure 14: Qualitative Result. Our method shows superior generation quality with character and scene consistency. 32 frames are generated by our method through diffusion forcing with bidirec- tional masking strategy. The prompt here is from the validation split with ChatGPT-40 re- re-imagined.
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Figure 15: Qualitative Result. Our method shows superior generation quality with character and scene consistency. 32 frames are generated by our method through diffusion forcing with bidirec- tional masking strategy. The prompt here is from the validation split with ChatGPT-40 re- re-imagined.
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H LONG-CONTEXT STRESS TEST DETAILS
We evaluate the robustness of our model under long-range dependencies by scoring sequences of keyframes-botth ground-truth and those sampled from our generated clips- with Gemini 2.5 Flash. Guided by the system prompt in Section H. the evaluator rates each sequence along six dimensions: character consistency, scene consistency, visual quality, aesthetics, diversity, and narrative coher- ence. An overview of the per-aspect trends is visualised in Fig. [I and the complete results for range of context lengths are reported in Tab.
INSTEMLPROMPT
expers visusl anslyst.
You will receive a saquence of movie keyframea latatic imagen)
in aapecta:
1. Character Conatstency
2. Scene Consistency
3. Visual Quality
Aesthetics
5. Diversity ino dupl leate or near-dup lcate framea)
Macrative Coherence
sequence sensible atezy??
For each aspect, assign 015 score and provide a one-sentence
justification.
Score meanings: Meaning
Unaccaptable: completely fails expectations
Foor: major problems
Poor: noticesble flaws
Fair:
expectations
14
I Good; aolid, minoz imperfectione
near-perfect
Scoring
Character Consistency: clothing, hairstyle, facial features,
proportions
remain constant.
Consistency: lighting background stay
across
frames.
v1 viau Quality: frames are sha we' 1-exposed, and co calar-ba aar-balaneedd palette, contras: deliberate
Aesthetics: composi.ion, and pleasing.
Diversity: frames meaningfu'ly distinct; penalize repetition. Narrativa Coharence: fromed suggest logical progression; panal lize abrupt shifts.
Return valid JSON in the following structure:
"character, srtrrattrr_onniiteeeey...
"score": <C-5>, *justification": *<text>"
"score": <0-5>, "just ieatifioan riattionn "<texts"
"yisual_quality":
recoze": <0 5*, "justirication": "clexts"
"sesthetios":
"coor"": <0-5>, "juetification"; pation": "<cexts"
"diversity";
recore": <0-52, "justification "clexts"
"narrative_colee "bazretive__cheerencee" :
"scoze": 5s, "juscification";
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Published as a conference paper at ICLR 2026
I OUT-OF-DOMAIN IDENTITY PRESERVATION GENERALIZABBILITY
We assess model's s ability to preserve unseen identities under noisy conditioning. For each out- of-domain individual, we collect photographs with accompanying captions. Twelve images serve interleaved context; their visual embeddings are perturbed with Gaussian noise for robust ditioning. The remaining four captions are then used to prompt the model, which must synthesize four frames of the same person. Qualitative results shown in Figs. T6 and 18 (generation with different dressing) and Figs. 17 and [9 (generation with different dressing). Our interleaved keyframe generator effectively maintains identity and accurately follows the given prompting in- struction (appearance descriptions), demonstrating strong generalization to unseen characters.
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Test Differrnt Dressing. Each test case begins with 12 randomly sampled context images that are injected with noise; the model then generates another four frames conditioned on textual prompts as well as the pretext interleaved conditions. Our interleaved tey-frame generator accurately maintains the character's 's identity, demonstrating strong out-of-domain generalizability.
Figure 16: Out-of-domain Identity-Presentt
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Published as a conference paper at ICLR 2026
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Test : Same Dressing. Each test case begins with 12 randomly sampled context images that are injected with noise; the model then generates another four frames conditioned on textual prompts as well as the pretext interleaved conditions. Our interleaved key-frame generator accurately maintains the character's identity, demonstrating strong out-of-domain generalizability.
Figure 17: Out-of-domain
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Published as a conference paper at ICLR 2026
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Figure 18: Out-of-domain Meettty-PPreservann Test : Different Dressing. Each test begins with 12 randomly sampled context images that are injected with noise; the model then generates another four frames conditioned textual prompts as well the pretext interleaved conditions. Our interleaved key-frame generator accurately maintains the character's 's identity while follows the instruction prompts, demonstrating strong identity preservation ability to oft-oi-domain characters.
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Published as a conference paper at ICLR 2026
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Test : Same Dressing. Each test case begins with 12 randomly sampled context images that injected with noise; the model then generates another four frames conditioned on textual prompts as well as the pretext interleaved conditions. Our interleaved key-frame generator accurately maintains the character's identity, demonstrating strong out-of-domain generalizability.
Figure 19: Out-of-domain
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Published as a conference paper at ICLR 2026
J DIFFERENCE TO FRAMEPACK
Framepack (Zhang & Agrawala 2025) a concurrent work on arXiv (confirmed with the authors), which proposes a similar token compression mechanism through context history down-sampling with exponential ratios (e.g., 1. 2, 4 ...). Below are explanations to several major difference in the aspects of motivation, applied scenario, technical design and empirical performance.
Motivation. While we share similar goal to reduce the GPU memory usage, Framepack motivation to enable consumer level GPU be capable large video generation model finetuning. Our movitation is to extend longer context as memory for preserving visual consistency.
Applied scenario. Framepack still focuses on classic single video clip generation and the training task is like image editing at first (generate last frames) and then do first-last frame video interpola- tion, which illustrated in Fig. 20 Framepack supports motion difference with a major entity across frames but do not support scene moving even scene changing. In contrast, our method is designed for multi-scent scene long video generation and the GoldenMem focuses on discrete keyframe generation whose task is more complex and challenging frame-level generation, illustrated in Fig. 21
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Figure 20: Framepack firstly edits, then interpolates. Framepack still focuses on classic single- clip video generation but in an anti-drifted auto-regressive way firstly generates last frames, and then interpolates the middle frames given the context of the first frame and last frames.
Technical design. Framepack uses aggressive compression rate for context compression (down- sample ratio is 2 or 4) with larger group sizes and introduces additional convolution kernels for the compression operation. In contrast, we use a fixed number frames for each group of context and use the fibonacci ratio (~ 1.618) for history downsampling in pixel space directly.
Empirical performance. We follow the typical context compression setup proposed in Framepack fikl F2k2f1 6k4 for empirical comparison to replace GoldenMem compression setup un- der the 32-frame generation setting. As Framepack's 's downsampling ratio and strategy is more ag- gressive, worse visual consistency and visual quality than GoldenMem setup and even slower convergence. So, we believe GoldenMem design is currently a more optimal solution for long-context multi-scene keyframe generation in discrete way.
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Published as a conference paper at ICLR 2026
Figure 21: GoldenMem is designed for multi-scene discrete generation. GoldenMem needs to understand and align different scenes and characters for keeping visual consistency and also keep high visual quality. In contrary to continuous frame interpolation, method is for discrete cross- scene keyframe generation which is more complex and challenging.
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Reviews:
[{"review_id": "l4oIM7kP9U", "reviewer": "Reviewer_c7bN", "summary": "This paper introduces \"Captain Cinema,\" a novel framework for generating short, multi-scene movies from detailed textual storylines. The core idea is a two-stage process: 1) a \"top-down keyframe planning\" stage, which generates a sequence of narratively coherent keyframes that act as a storyboard, and 2) a \"bottom-up video synthesis\" stage, which generates the video motion between these keyframes. To tackle the challenges of long-range consistency and exploding context length, the authors propose several key contributions. The most significant is \"GoldenMem,\" a memory compression mechanism that stores past keyframes at progressively lower resolutions based on an inverse-Fibonacci sequence derived from the golden ratio, allowing the model to maintain a long visual history with a fixed token budget. The framework also incorporates a hybrid attention mechanism for Multimodal Diffusion Transformers (MM-DiT), progressive long-context finetuning, and dynamic stride sampling to enable stable and efficient training on long-form cinematic data. The authors demonstrate through extensive experiments, including a long-context stress test and detailed ablations, that Captain Cinema can generate narratively and visually consistent short movies that surpass the capabilities of existing methods.", "questions": "**Major Comments**:\n1. What was the rationale for choosing the golden ratio specifically, beyond the aesthetic connection to the \"golden ratio squares\" analogy? How sensitive is performance to the golden ratio choice? Have you tried φ variants (e.g., 1.5, 2.0) or adaptive learned scales?\n2. Masking schedule: Do you train with bidirectional global attention and switch to causal at inference? Please clarify the exact masking configurations for both training and inference.\n3. How is the hybrid attention masking applied differently during training and inference? Can you provide quantitative or qualitative evidence of its impact on model performance?\n\n**Minor Comments**:\n1. I agree that multi-scene movie generation is a meaningful and valuable task. The proposed approach—first generating key frames and then extending them into a video—is conceptually similar to story visualization tasks (e.g., ConsiStory, DramStory, StoryBooth). These related works deserve more thorough discussion and comparison.\n2. The overall paper reads more like a technical report, providing only a brief overview of the methods. It would be more convincing if the authors could elaborate on the insights and motivations behind the proposed techniques.\n\nI would be happy to raise my overall score if all concerns are properly addressed.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The overall framework is simple and makes sense. It mainly consists of a two-stage Architecture: The decomposition of the problem into top-down planning (storyboarding) and bottom-up synthesis (animating) is logical and effective. This enables multi-scene generation with better narrative coherence.\n2. GoldenMem Context Compression: The GoldenMem technique introduces an inverse-Fibonacci downsampling protocol, maintaining rich long-range context while keeping token budgets tractable. Ablative analysis convincingly demonstrates that this enables longer context windows and computational savings without major quality loss.\n3. Visualization and Analysis: The figures are diverse and directly illustrate methodological claims and empirical results.", "weaknesses": "1. **Limited Theoretical Analysis of GoldenMem**: The mathematical treatment of GoldenMem in Section 3.2, while operationally clear, lacks a principled analysis of compression trade-offs. The geometric decay is motivated by token counts, but there is little insight into semantic loss or the optimality of the selection policy. For example, while the summation gives a neat token bound, the information preserved under this decay is not theoretically or empirically analyzed—could less aggressive or adaptive schemes outperform?\n2. **Context Selection & Semantic Retrieval Ambiguity**: The approach for semantic-oriented context fetching via CLIP/T5 is described but lacks sufficient detail for reproducibility and interpretation. For example, in Figure 5, it's claimed that CLIP-based retrieval leads to better coverage—but how is \"coverage\" computed? There is no ablation showing the impact on downstream movie coherence or consistency.\n3. **Unclear hybrid attention mechanism**: The paper provides insufficient clarity on how the hybrid attention masking is applied in training versus inference.", "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": "QDSYSzzDzL", "reviewer": "Reviewer_uMzc", "summary": "This work introduces Captain Cinema, a DiT-based framework for short movie generation. The key idea of this framework lies in bridging the top-down and bottom-up interleaved generation paradigms. Given the storyboard, a top-down keyframe planner first produce a sequence of key frames, then a bottom-up video generator interpolates full motion conditioned on these keyframes. In addition, a memory-efficient mechanism (i.e., GoldenMem) is proposed to address the explosive increase in token budget. Comprehensive evaluation and analysis demonstrate the satisfactory visual quality, consistency, and efficiency of the proposed method for multi-scene video generation.", "questions": "1. I'm curious why this work uses a keyframe-based approach for multi-scene video generation, instead of a native approach like LCT?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. This work makes a preliminary exploration of multi-scene, whole-movie generation. Its data acquisition and processing, as well as the proposed keyframe planning and keyframe-conditioned video generation, will contribute to the community and inspire future work.\n\n2. The introduced GoldenMem mechanism is technically sound and effective, significantly reducing the token budget under long contexts, as shown in Figure 4. Both GoldenMem and Hybrid Attention Masking alleviate computational explosion in long-context visual generation.\n\n3. The experiments are solid and comprehensive, including qualitative and quantitative evaluation, ablation analysis, and stress testing.\n\n4. The paper is clear and well-structured.", "weaknesses": "1. There appears to be no qualitative comparison between Captain Cinema and existing methods. Based on the provided videos, the diversity and richness of the generated scenes are still limited, with no significant difference compared to LCT.\n\n2. The GoldenMem mechanism is somewhat similar to FramePack, which diminishes its novelty.\n\n3. The keyframe-based approach cannot guarantee temporal consistency between shots, and the proposed keyframe-conditioned video generation model does not seem to be able to alleviate the problem either.\n\n4. Figure 1 claims that Captain Cinema can generate multi-scene videos longer than 1,000 seconds, but no demo video or evidence of this is provided.\n\n5. L254-L255: \"geneeration\" -> \"generation\"", "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": "YmGawiteGs", "reviewer": "Reviewer_2sQo", "summary": "Captain Cinema proposes a two-stage framework that first performs top-down keyframe planning from a detailed movie storyline and then uses bottom-up video synthesis conditioned on those keyframes, training a Multimodal Diffusion Transformer (MM-DiT) with an interleaved strategy on a curated cinematic dataset to produce short multi-scene movies with long-range coherence. GoldenMem is the paper’s long-range memory mechanism that stores a small set of high-quality “golden” keyframe representations (visual and/or multimodal embeddings) and uses them as persistent anchors during interleaved training and inference to maintain character, scene, and stylistic consistency across many scenes and long context window.", "questions": "1. What exactly is the interleaving schedule and curriculum for MM-DiT (sequence lengths, mixing ratios, optimization schedule), and how sensitive are results to these hyperparameters?\n\n2. Does the system include mechanisms (e.g., latent sampling, clarification prompts, fallback heuristics) when the storyline lacks detail, and how does output diversity vs. fidelity trade off in those cases?\n\n3. Are there explicit modules or losses that enforce frame-to-frame continuity, or is it emergent from training?\n\n4. What are the common, reproducible failure modes (e.g., broken limbs, character swapping, sudden lighting jumps), and are there proposed mitigation strategies?\n\n5. How well does the model adapt to different cinematic genres, lighting conditions, actor types, or animation styles; is fine-tuning required per style?\n\n6. What are the sources and licensing terms of the curated cinematic dataset, how diverse is it, and how might dataset bias affect what stories/styles the model can generate?\n\n7. How does the system prevent or detect generation that imitates living actors or copyrighted content without consent, and are there guardrails against misuse?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Clear decomposition into planning + synthesis, separating long-range narrative/keyframe planning from local spatio-temporal generation addresses coherence at different timescales and makes the overall task more tractable.\n\n2. Long-context modeling focus of adapting MM-DiT with an interleaved training strategy targets one of the core failure modes of generative video (losing global narrative or character consistency across scenes).\n\n3. Use of keyframes as conditioning signals where explicit keyframes act as strong structural constraints that likely improve framing, character consistency, and scene layout compared with unconstrained long-video diffusion.\n\n4. Building a dataset of interleaved data pairs tailored to multi-scene narrative generation helps align training data to the intended output domain and probably improves qualitative results.\n\n5. Practical pipeline for short-movie use case, the paper presents an end-to-end system that maps a storyline to a finished short movie, which is a meaningful step toward usable creative tools for filmmakers and storytellers\n\n6. GoldenMem keeps long-range visual anchors that directly enforce consistency for recurring characters and settings, which is a central failure mode in multi-scene movie generation. Compact and computationally efficient relative to storing entire past frames or attending over extremely long context lengths, improving scalability for multi-scene generation. It is easy to integrate into a two-stage pipeline (planner → keyframes → generator) because golden entries are naturally derived from planned keyframes and can be updated or replaced as the storyline evolves", "weaknesses": "1. The method’s success hinges on the generated keyframes accurately reflecting the narrative and desired visual style; failure modes or hallucinated keyframes could produce hard-to-correct artifacts in the final video. Analysis on that could be useful. \n\n2. Conditioning on keyframes constrains endpoints, but plausible and temporally coherent transitions between them remain challenging; motion realism, continuity of lighting, and consistent character actions may still suffer. Any comments on that could be useful. \n\n3. Cinematic quality and narrative consistency are subjective; the paper relies heavily on qualitative examples and user studies rather than standardized, reproducible metrics, making comparisons and objective evaluation harder.\n\n4. MM-DiT long-context training with interleaved pairs and high-resolution cinematic data  requires substantial compute and curated data, raising barriers to replication and broader adoption.", "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": "ikXEzRvOkw", "reviewer": "Reviewer_TXMh", "summary": "This paper presents Captain Cinema, a framework for generating short, narrative-driven movies from a detailed textual description. The method employs a two-stage process: a \"top-down\" planning step that generates a sequence of keyframes to ensure narrative and visual coherence, followed by a \"bottom-up\" video synthesis step that generates the motion between these keyframes. The authors introduce a visual context compression technique called GoldenMem and an interleaved training strategy to handle long-form cinematic data.", "questions": "I have reviewed this paper during NIPS2026, where I rejected this paper due to the novelty. As the contributions are unchanged, I still kept my origin rating in NIPS2026.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper tackles the ambitious and important challenge of long-form, multi-scene video generation, moving significantly beyond the single-shot clips that current models typically produce. This hierarchical approach of planning keyframes first and then synthesizing video is an intuitive and powerful way to maintain long-range consistency.\n\n2. The proposed GoldenMem technique for visual context compression is a clever and efficient solution to manage the otherwise exploding computational cost of long video sequences. This method effectively preserves historical context with a bounded and minimal overhead.", "weaknesses": "This hierarchical style to produce narrative videos has been utilized in many previous papers, such as MovieDreamer, VideoDirectorGPT, which weakens the novelty of this paper. And the proposed Goldmem is overclaimed in my opinion.", "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)