SceneDecorator: Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency
Authors: Quanjian Song
Donghao Zhou
Jingyu Lin
Fei Shen
Jiaze Wang
Xiaowei Hu
Cunjian Chen
Pheng-Ann Heng
Venue: NeurIPS 2025 poster ()
Keywords: Story Generation
Scene Planning
Scene Consistency
Primary area: applications
TL;DR: Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency

Abstract: Recent text-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus on character consistency, they often overlook the crucial role of scenes in storytelling, which restricts their creativity in practice. This paper introduces scene-oriented story generation, addressing two key challenges:  (i) scene planning, where current methods fail to ensure scene-level narrative coherence by relying solely on text descriptions, and (ii) scene consistency, which remains largely unexplored in terms of maintaining scene consistency across multiple stories. We propose SceneDecorator, a training-free framework that employs VLM-Guided Scene Planning to ensure narrative coherence across different scenes in a ``global-to-local'' manner, and Long-Term Scene-Sharing Attention to maintain long-term scene consistency and subject diversity across generated stories. Extensive experiments demonstrate the superior performance of SceneDecorator, highlighting its potential to unleash creativity in the fields of arts, films, and games.

SceneDecorator:
Towards Scene-Oriented Story Generation with Scene Planning and Scene Consistency
Quanjian Song!-*, Donghao Zhou 12,
Jingyu Lin 12, Fei Shen', Cunjian Chen Pheng-Ann Heng"
Jiaze Wang", Xiaowei Hu
'Monash University The Chinese University of Hong Kong
National University of Singapore
"South China University of Technology
Project Page: htttp::/ gi github..... ictceeeeeeee
Theme:
Local Ncenes
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manages to "decorate" the scenes of story images, ensuring narrative coherence across different scenes (green arrow) and scene consistency across different stories (blue arrow), all based on a concise user-provided theme.
Figure 1: Overview of SceneDecorator.
Abstract
Recent text-to tort-t--iie eext-to-image models have revolutionized image generation, but they still struggle with maintaining concept consistency across generated images. While existing works focus on character consistency, they often overlook the crucial role scenes in storytelling, which restricts their creativity in practice. This paper troduces scene-oriented story generation, addressing two key challenges: (i) scene planning, where current methods fail ensure scene-level narrative coherence by relying solely on text descriptions, and (ii) scene consistency, which remains largely unexplored terms of maintaining scene consistency across multiple stories. We propose SceneDecorato
training-fie free framework that employs VLM-Guided Scene Planning to ensure narrative coherence across different scenes a "global- to-local" manner, and Long- Long-Term Scene-Sharing Attention to maintain long-term scene consistency and subject diversity across generated stories. Extensive experi- ments demonstrate the superior performance of SceneDecorator, highlighting its potential to unleash creativity in the fields of arts, films, and games.
'Work done at Monash University.
Eqqual contribution. Project leaders. &Correpponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Text-to-image (T2I) models 1 3 4 have demonstrated impressive proficiency in generating high- quality images from text descriptions. However, they struggle maintain concept consistency across generated images due to their stochastic nature [5]. Such consistency holds significant commercial value and application potential in education [6], art g and entertainment [2] underscoring the
need for the task of story generation that can create multiple images with consistent concepts [TO]
Considering the significance of story generation, numerous prior studies have been devoted ad- vancing this important task. Early studies such as PorotoSV [12] and FlintstonesSV [3] are typically trained on given datasets with consistent characters. These methods achieve decent performance in specific domains but are inherently limited in generalization. Leveraging the exceptional generation quality of diffusion models, subsequent works 14 15 17 have begun exploring open-domain characters. This advancement achieves a compelling balance between realism and aesthetics.
Although existing story generation methods have made significant progress in character consistency, they usually overly focus on preserving characters while neglecting scene depiction, which is equally crucial for conveying the narrative of stories [8] In light of that, the motivation for this paper arises: How can we achieve story generation from the perspective of scenes? In this work, we formulate Scene planning: Existing
scene-oriented story generation, which presents two primary challenges: approaches generate the scenes of story images solely based on text descriptions, leading to lack of scene-level narrative coherence. This coherence also plays a vital role in enhancing storytelling visual fluency. (ii) Scene consistency: In practical scenarios like film storyboarding [19), it is crucial to generate diverse story images with consistent scenes that align with different plots and characters. Maintaining long-term scene consistency across multiple stories remains underexplored.
This paper introduce SceneDecorator, training-free framework for scene-oriented story generation (see Figure D. aimed at addressing the above challenges. SceneDecorator contains two key techniques:
(i) To tackle scene planning, we develop a VLM-Guided Scene Planning strategy. It utilize the visual perception of Vision-LanguuggMModdel (VLM) to create scenes and story sub-prompts in a "global-to-local" manner. Specifically, this strategy begins with VLM that interprets the user- provided theme to generate a corresponding global scene description. The description is then passed into an off-the-shed image generator to create a meaningful global scene image. Finally, this image is further deconstructed by the VLM into multiple local scenes and story sub-prompts, serving as the basis for subsequent story generation. This scene planning strategy ensures scene-level narrative continuity, as the local scenes are derived from a global scene with shared scene semantics.
(ii) To maintain scene consistency, we design novel Long-Term Scene-Sharing Attention mech- anism. Specifically, it first employs a Mask-Guided Scene Injection module, which enhances the IP-Adapter (20) with cross-attention masks to guide fine-grained scene injection, thereby ensuring subject style diversity. Then, the latent representations interact across scenes through a Scene-Sharing Attention module during the denoising process, thereby preserving scene consistency across generated stories. Furthermore, this attention module is further extended by an Extrapolable Noise Blending scheme, thereby achieving long-teny scene consistency across stories with low overhead.
Extensive qualitative and quantitative comparisons validate the effectiveness of our Sccnetecorator, with ablation studies and diverse applications showcasing its robustness and versatility.
2 Related Works
Controllable Text-to-Image Generation. Given the ambiguity of textual descriptions in guiding
image style 21 R content [22 23), and layout 24, many works have been dedicated to enhancing control in text-to-image (T2I) generation. Prior works like ControlNet [25] and T21-Adapter [26]
tackle this challenge by employing trainable modules. These modules enhance control over visual
style and spatial organization, making them more effective than naive T2I models [27 28 29 30 In
addition, some studies have also explored several advanced techniques like prompt engineering [3
and cross-attention constraints [2 33 enabling better generation regulation. Moreover, some
approaches focus on visual content generation with diverse task paradigms (34
35
36
37
while
others focus more practical generation in real-world applications 39
40
43

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(a) VLM- Scene Planning
(b) Long-Term Term Scene-Sharing Attention
Figure 2: Overall framework of SceneDecorator. (a) VIM-Guided Scene Planning involves con- ceptualizing, visualizing, and crafting in a "global-to-loca"" manner. (b) Long-Terne Term Scene-Sharing Attention maintains long-range scene consistency and subject diversity across generated stories.
Story Generation. Owing the success of diffusion models, many recent works have applied them to story generation, showcasing significant value in real-world applications. Initially, AR-
LDM 44 uses auto-regressive paradigm for story generation, while Make-A-Story 45 integrates
a visual memory module for aggregation. Subsequently. some researchers attempt to leverage large language models (LLMs) for coherent story generation. For example, Sto-yGPT-V (46) uses LLMs to resolve ambiguous references and maintain context, while SEED-Story [47] combines image-text data to generate coherent story images. Recently, some works 48 49 50 have begun to focus on character consistency in story generation, ensuring that their identity remains intact across diverse text descriptions. Building on this foundation, other studies [5] manipulate attention maps to achieve training-fie free story generation while maintaining character consistency. However, these methods overlook the planning and consistency scene contexts story generation, which also play fundamental role in visual storytelling. Our work seeks to resolve these problems systematically.
3 Methodology
3.1
Overall Pipeline
In this work, we design training-free framework called SceneDecorator, to address two key challenges story generation: scene planning and scene consistency. The overall framework of SceneDecorator is illustrated in Figure which comprises two core techniques: (i) VLM- VLM-Guided Scene Planning. Leveraging a powerful "ision-Language Model (VLM) as a director, it decomposes user-provided themes into local scenes and story aub-prompts in "global-to-local" manner. (ii) Long-Term Scene-Sharing Attention. By simultancously integrating guiid scene injection, scene-sharing attention, and extrapolable noise blending, it maintains subject style diversity and long-term scene consistency in story generation. We elaborate on these in the following sections.
3.2 VLM-Guided Scene Planning
Relying solely on text descriptions to generate story images often lacks scene-level narrative co- herence. In real-world applications like filmmaking, a global scene is first established, and then local scenes are derived to unfold different narratives. Inspired by this application, we propose VLM-Guided Scene Planning, which leverages the visual understanding of VLM to unfold scene shots and related narratives in "globallto-local" manner. The overall process is decomposed into three core steps: (i) Conceptualizing the Global Scene, (ii) Visualizing the Global Scene, and (iii) Crafting Local Scenes and Sub- cab-prompts. In the following, elaborate each stage in detail.
Conceptualizing the Global Scene. first leverage powerful VLM to provide comprehensive global scene description. Specifically, when users provide a theme T. we expect the VLM Fg to fully exploit its scene imagination capability and generate global scene description Q = (T( related to the given theme T. Moreover, further enhance VLM performance by leveraging its
3

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Figure 3: Comparison of different methods. In (a), subject styles align with the scene but at the expense of diversity, whereas (b) better showcases diversity. Compared to (c), (d) further emphasizes scene consistency. Note that purple boxes highlight distinctions. Best viewed with zoom-in.
in-context learning ability. Specifically, we provide illustrative examples to guide the model toward more accurate outputs, with the example details presented in the supplemental material.
Visualizing the Global Scene. Based on the global scene description Q produced by the VLM Fo above, we then employ a powerful off-the-shelf T2I model, like FLUX.1-dev [5]]. generate meaningful global scene image V. In summary, using the complementary capabilities of the VLM and the T2I model, we transform the abstract theme T into an immersive global scene image V. This scene image establishes the global foundation for subsequent local storyline creation.
Crafting Local Scenes and Sub-prompts. Building upon the generated global scene image V above, we further leverage the powerful perception capability of the VLM to intricately create local storylines, which contain relevant local scenes and story sub-prompts. Specifically,
expect the VLM Fa to act as an imaginative director: based on the user-provided theme T. determines the coordinates {C Ji= for M storyboard scenes within the global scene image V. Then, the global scene image is Ti=-- This procedure is formulated below:
cropped accordingly to extract final local scenes (V*)
M M R=1 = Crop(V, (M))))) where M Fe(V.T).
(1)
Finally, for each cropped local scene V and the corresponding theme T, employ the VLM Fa to generate sequential story sub- sub-prompts pI:N This process can formulated as follows:
pI:N Fo(v T), i
This scene planning framework transforms the user-provt nee--provdee abstract theme T into multiple local scenes and corresponding story sub-prompts, creating cohesive narrative that enhances storytelling. The detailed prompts used by the VLM in each step are provided in the supplemental material.
3.3 Long-Term Scene-Sharing Attention
Once each local scene [ is established, is typically combined with corresponding story sub-prompts pl:N for subsequent story generation, which is similar to film storyboarding [19]. During generation, we propose Long-Tern Term Scene- Sharing Attention address the challenge of scene consistency that overlooked in prior work. First, Mask-Guided Scene Injection is developed to pre- serve the diversity of subject style while achieving scene injection. Next, Scene-Sharing Attention is utilized to maintain scene consistency across multiple stories. Furthermore, this attention mechanism further extended through Extrapolable Noise Blending achieve long-term consistency with low memory overhead. The details of these components are described in the following paragraphs.
Mask-Guided Scene Injection. Achieving scene consistency first requires the effective injection of visual semantics from the given scene. One straightforward approach is IP-Adapter [20]. which enhances representation by integrating visual and text prompt through decoupled cross-attention mechanism. However, as shown Figure (a), direct using IP-Adapter for scene injection preserves overall semanties but makes the subjects blend too tightly with the background, which reduces the style diversity across generated stories. To address this issue, we improve IP-Adapter with cross-attention masks guide fine-grained scene injection, thereby ensuring subject style diversity.
During the cross-attention process, local scene V and story sub-prompt Pj are first encoded into hidden features and then mapped Ky, Vg and Ke, Vo via respective weight matrices. Next, the

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Algorithm Extrapolable Noise Blending
Input: T2
The time interval of noise blending The numbers of generated stories The text descriptions of different stories
Input: N
Input: pli
Input: V
The visual prompt of Diffusion model, noise scheduling Different stories generated by the model
Input: Es, DDDMMcchedule
Output: {1')(=
for t = T.T 1,.'-1,...,333. 3,2
1:NV <- 0
Stmp
if 2 T1 and < then
S + [i,
I.....
norm < N
for (i.j) E E do
81, + Eg (2) ,,, A,P.
# Mask- Lauk-Guided Scene Injection and Scene-Sharing Attention
Cmpp Cemp + E1
Smp Smmp + E2
end for
else
norm t-1 1
for k = 1, 1, N - N. do
Eimp Etmp + Ez( pt V)
General denoising
end for end if Z < DDIMMSchcchlll(eemg//oooooe Ze
1:N
# Blend the noises
end for Ju:N D(Z
Return: [
latent representation mapped Qe and multiplied by Ke and K' generate attention maps:
KT Qe
Qc KJT vd
A. Softmax
A =
vd
where d is the dimension of Qu and Ke. Ar (Rw))x represents the cross cross-attention map between text and generated image, where hw denotes the number of image tokens and L. indicates the number of text tokens. A' denotes the Pross-attention map between scene and generated image.
At each denoising step, the cross-attention map A. is averaged over all previous steps. The subject token of the sub-prompt pj is then selected, and its activation region in A', is used as the masks M E Axx, Finally, the cross-attention maps A. and A', are multiplied by V. and V/ respectively, and the results are combined through an element wise weighted sum with the subject masks M:
Leew A. V. + A (1- A'.V''
(4)
where À is weighting factor that balances scene features and text features. As shown in Figure Fig, this approach ensures effective scene injection while enhancing the diversity of subject styles.
Scene-Sharing Attention. The above cross- attennttoo mechanism effectively injects scene semantics, achieving scene consistency across generated stories to some extent. However, as shown in Fi there is an inherent conflict between story sub-prompts and scene consistency, which significantly weakens coherence across generated stories. resolve this issue, we extend self-attention with scene-sharing attention to further enhance scene consistency between generated stories.
During the self-attention process, the latent representations from dual-branches are mapped to Q,K, V and Q K, V through their weight matrices. As depicted in Figure each branch then attends the K and V of the other branch for scene interaction, with the masks . applied restrict attention to the background. The new key K' and new value V' are formulated as follows:
K' K, о (1 M)). -V= (1 (1-M)
(5)
where [+] represents the concatenation operation and @ denotes element- wise product operation. It is noted that the subject masks M for the other branch are derived using the same method outlined in Guided Scene Injection and are therefore omitted here for brevity.
5

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Table I: Quantitative comparison of automatic metrics and user study across other baselines. The best result marked in bold, and the second- best is underlined.
Automatic Metrics
User Study
Methods
CLIP-T
DreamSim-T
DINO-F1
Text Align.
Scene Align.
Image Qual.
CustomDiiftusion [52
0.306
0.752
0.373
7.9%
3.4%
6.0%
ConsiStory [S]
0.320
0.723
0.475
21.3%
14.1%
24.7%
StoryDiffusion
14
0.311
0.735
0.340
14.3%
6.3%
11.8%
SceneDecorator (Ours)
0.312
0.605
0.571
56.5%
76.2%
57.5%
Finally, the Q and Q from each branch will perform attention with the new K" and V respectively:
Attention K", V"), Attention (Q, K", V').
As shown in Figure d), this mechanism allows different stories attend each other's scene informa- tion during the self-attention process, further enhancing scene consistency across stories.
Extrapolable Noise Blending. Although the above method ensures scene consistency across stories, it is limited to generating two stories. We propose an extrapolable noise blending scheme, achieving long-term scene consistency across multiple stories with low overhead, as shown in Algorithm
To simultaneously generate N stories with consistent scenes, we extend the Scene Sharing Attention module with noise blending during the denoising interval t E [Ti 72). Specifically, the latent representations {Z₃} Fi=1 dynamically partitioned into complementary pairs Z Z >, with i, E 1...., N, allowing each story to participate N pairings per denoising step. The noise predicted for each story in different pairs is then averaged to further update the latent representations. This noise blending strategy enables scene interaction across multiple stories while requiring the GPU memory usage of only stories, therefore ensuring significantly lower overhead.
4 Experiments
4.1 Experimental Setups
Implementation Details, We leverage Dw [53] as the VLM to guide scene planning, FLUX.1 1- dev [51] as the off-the-shelf T2I model to generate global scenes, and SDXL [54] the base model collaborate with the proposed techniques for story generation. Additionally, we employ IP-Adapter-
XL 20 support extra scene input. The hyperparameters are set as follows: M 4, N T = 0, and T2 = 25. SceneDecorator can run on a single RTX 3090 GPU without further training.
Baselines and Datasets. Since our work is the first focus on scene-oriented story generation, there are no directly related comparison methods. Therefore, we select and adapt three baselines: CustomDDiflusion [52] ConsiStory (5). and StoryDiffusion [14] due their competitive performance on similar tasks. To validate the effectiveness of SceneDeccoatoo,, we use GPTT-40 (55) to randomly generate 146 themes across different domains. Each theme is then decomposed into 4 distinct local scenes by the VLM, with each scene containing story sub-prompts. In total, there are 2,920 scene-prompt pairs that serve as input for each method, ensuring a fair comparison.
4.2 Quantitative Comparisons
We validate the superiority of our ScemeDecorator from two perspectives: Automatic Metrics that provide an objective assessment, and User Study that offers a subjective evaluation.
Automatic Metrics. We evaluate the quality of the generated stories from three dimensions: (i) CLIP-T [56), which assesses the alignment between the generated stories and the input prompt, (ii) DreamSim-I [57], which measures the alignment between the generated stories and the input scene, and (iū) DINO-F (58) which evaluates the scene consistency across generated stories. The detailed results are reported in T SceneDecorator outperforms all other methods in both the DreamSim-1 and DINO-F metrics, with ConsiStory [5] ranking second. This indicates that our method achieves the best performance in scene alignment and consistency. In the CLIP-T metric, our SceneDecorator ranks

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Figure 4: Qualitative comparison of our SceneDecorator with other baselines. SceneDecorator demonstrates superior scene consistency and alignment across different stories compared other baselines, making it well-suited for creative applications in filmmaking. Best viewed with zoom-in.
second, slightly behind ConsiStory, Overall, SceneDecorator demonstrates superior performance across these metrics, showing its effectiveness in scene-oriented story generation.
User Study. We designed questionnaire with 13 groups of generated results, where each group contains four different stories. Questionnaires are randomly distributed to participants from diverse countries, cultural backgrounds, genders, and age groups, inviting them to select the best result from each group based on three key aspects: text alignment, scene alignment, and image quality, Ultimately, we have received 61 valid responses, with the detailed results illustrated in Table Our SceneDeccorator achieves state-of-the-ar performance across all three aspects, demonstrating particularly significant gaps in scene alignment. This success can be attributed to our innovative VLM- Guided Scene Planning strategy and the advanced Long-Term Scene Shene-Sharing Attention mechanism.
4.3 Qualitative Comparisons
In addition, we conduct qualitative comparisons of the proposed SceneDecorator with three existing approaches, including CustomDDiffusion [52], StoryDiffusion [14, and ConsiStory (5] The visual- ization results are presented in Figure and additional results can be found in the supplementary material. CustomDiffusiin, which is designed for personalized characters, faces challenges in gen- erating personalized scenes. Similarly, StoryDiffusion, which focused on consistent character story generation, struggles to maintain scene consistency across different stories. On the other hand, Consistory demonstrates strong performance in preserving scene consistency but encounters difficulties in effectively capturing the full scope of scene information, limiting its versatility. In contrast, our SecnrDecorator efficiently capture detailed semantics of the scene while ensuring scene consistency across generated stories, showcasing its superiority in oriented story generation.
4.4 Ablation Studies
In this section, we explore the effectiveness of the three proposed components: Mask-Guided Scene Ijjction, Scene-Sharing Attention, and Extrapolable Noise Blending, individually.
Mask-Guided Scene Injection. We compare the generation results with and without the mask-guided scene injection module, with qualitative examples shown in Figure Without mask-guided scene injection, textual descriptions alone fail to generate stories that contain specific scene semantics. In contrast, incorporating mask-guided scene injection provides fine- mna-grd guidance, which not only facilitates the injection of scene semantics but also preserves diversity in subject styles.
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Scene Injection and Scene-Sharing Attention. "V" and " X* indicate whether each component used. The synergy between these components ensures scene consistency and subject diversity across generated stories.
Figure 5: Ablation study of the two components:
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Figure 6: Comparison of long-term consistency with and without Extrapolable Noise Blending, Our approach ensures consistent scenes with low overhead, while effectively preserving subject diversity throughout the entire narrative, leading to more cohesive results. Best viewed with zoom-in. in.
Table 2: Efficiency analysis of Extrapolable Noise Blending. "OOM" represents out of memory.
Number of Generated Story Images
Methods
2
5
10
15
20
25
w/o Extrap.
11.4G
12.7G
14.5G
17.5G
20.4G
23.5G
00M
Noise Blending
w/ Extrap.
11.4G
12.7G
12.7G
12.7G
12.7G
12.7G
12.7G
Noise Blending
Scene-Sharing Attention. Moreover, the effectiveness of the acene-sharing attention mechanism is also demonstrated in Figureā3] where its impact on maintaining narrative coherence is clearly illustrated. By leveraging this proposed attention mechanism, the model able to enhance scene consistency across different generated stories, ensuring that key contextual elements remain fully aligned and logically connected throughout the narrative. When combined with the scene injec- tion strategy, this synergy not only ensures that subject diversity is effectively integrated into the storytelling process but also reinforces scene consistency across different generated stories.
Extrapolable Noise Blending. Finally, we assess the effectiveness of extrapolable noise blending in generating stories that preserve long-ten tergttemm scene consistency, with the associated experiments performed on a single RTX 3090 GPU. The visualization result is illustrated in Figurejo and the memory usage is reported in Table Although scene consistency can partially maintained without extrapolable noise blending, the associated memory usage scales with the number of story images and often causes out-of-memory (OOM) errors. In contrast, applying extrapolable noise blending fixes memory usage, effecttivly preventing "OOM" issues while ensuring long-term scene consistency.

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Figure 7: More applications of our SceneDecorator. It can support generation with manual scene input (left) and generation with consistent characters (right). Best viewed with zoom-in.
4.5 Rationality Analysis of VLM-Guided Scene Planning
We further analyze the local scenes partitioned by the VLM-Guided Scene Planning strategy from two perspectives: Coordinate Rationality and Semantic Rationality, detailed below.
Coordinate Rationality. When partitioning the global scene image (Fquation Tb. the coordinates predicted by the VLM may occasionally fall slightly outside the defined image boundaries. To robustly address this issue, we apply a simple yet effective correction: invalid coordinates are automatically snapped to the nearest valid bounding box, thereby ensuring all generated coordinates remain usable.
Semantic Rationality. To assess the semantic rationality of the partitioned local scenes, we conducted quantitative evaluation using GPT- 4o [55] For each sample, the story theme. the global scene, the derived local scenes, and the corresponding story sub-promper are provided to GPT-40, which is instructed evaluate them along three key criteria: Narrative Coherence, Theme Adherence, and Layout Reasonableness. Each criterion was scored on a 10-level scale (0-100%), with the details illustrated in Table The results show that VLM-Guided Scene Planning exhibits strong robustness across narrative coherence, theme adherence, and layout reasonableness.
Table 3: GPT-4o evaluation of narrative coherence, theme adherence, and layout reasonableness.
Narrative Coherence
Theme Adherence
Layout Reasonableness
90.061
92.57%
90.29%
More Applications
Generation with Manual Scene Input. In addition to automatic VLM-Guided Scene Planning, SceneDecorator can also support manual scene input. Specifically, users can provide global scene, manually divide it into local scenes, and use them for subsequent story generation. As illustrated in Figure the impressive visual results further emphasize the scalability of SceneDecorator.
Generation with Consistent Character. Beyond generating stories with scene consistency, SceneDecorator can also generate stories that preserve character consistency under different scenes. Specifically, modify the scene-sharing attention by inverting the mask to ensure character consis- tency, while keeping the mask mask-guided scene injection unchanged. In Figure 7 the same character experiences different stories across scenes, further showcasing the flexibility of SceneDecorator,
Generation with Other Tools. As training-fres framework, SceneDecorator can also seamlessly integrate with other generative tools to meet diverse user needs. Detailed examples are demonstrated Figure It can combined with PhotoMaker [59] customized character generation and with
ControlNet [25 for precise conditional control. Furthermore, it can work effectively with stylized
LoRAs 600 to achieve diverse style generation. In summary, by incorporating diverse generative tools, proposed SceneDecorate highlights more creative and flexible workflows.

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Figure 8: More applications of our SceneDecorator, also support generation with other tools: PhotoMaker, ControlNet, and stylized LoRAs. Best viewed with zoom-it in.
Scene
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Figure 9: More applications of our SceneDeccrator. It can also support generation with evolving scenes, such transitions from morning to dusk (left) well as from summer winter (right).
Generation with Evolving Scenes. In addition, Secnraeco provides robust support for gener- ating multiple stories with evolving scenes, such as changes the time of day or shifts in seasons, which further showcases its flexibility and adaptability. As illustrated in Figure the model is capable of dynamically adapting to a wide range of scene inputs, enabling the generation of diverse stories that evolve seamlessly across different contexts. SceneDecorator empowers users to flexibly craft immersive and dynamic stories that capture the essence of change across diverse settings.
Conclusion
This paper introduces SccneDec,rator, a training-free It emphasizes scene planning and scene consistency, in contrast the character consistency focus of prior works. Our SceneDecoratee comprises two core techniques: (i) VLM-Guided Scene Planning, which decomposes user-provided themes into local scenes and story sub-prompts in "global-to-local" manner, and (ii) Long-Term Scene-Sharing Attention, which integrates mask-guided scene injection, scene-sharing attention, and extrapolable noise blending to maintain subject style diversity and long-term scene consistency during generation. Extensive experiments validate the effectiveness of SceneDecorator, showcasing its ability enhance creativity across real-world applications.
framework for scene-oriented story generation.
This work was supported by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government via the Hong Kong Centre for Logistics Roboties, by the Faculty Initiatives Research of Monash University (Contract No. 2901912), by the NVIDIA Academic Hardware Grant Program, and by the Research Start-up Fund for Prof. Xiaowei Hu at the Guangzhou International Campus, South China University of Technology (Grant No. K3250310).
Ackmowledgments.
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References
[1] I. Goodfellow, J. Pouget-Abadis M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and
Y. Bengio, "Generative adversarial networks," Communicutions of the ACM, 2020.
[]] A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and L. Sutskever, "Zero-shot text-to-image generation," " in International Conference on Machine Learning, 2021.
[3] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, "High-resolutio image synthesis with latent diffusion models," in IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[4] D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rom- bach, "Sdxl: Improving latent diffusion models for high-resolution image synthesis." arXiv preprint arXiv:2307.01952 2023.
[5] Y. Tewel, O. Kaduri, R. Gal, Y. Kasten, L. Wolf, G. Chechik, and Y. Atzmon, "Training- freee consistent text-to- mext-to-image generation." " in ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques, 2024.
[6] D. Kostons and B. B. de Koning, "Does visualization affect monitoring accuracy, restudy choice, and comprehension scores of students in primary education?" Contemporary Educational Psychology, 2017.
[7] Q. Song, M. Lin, W. Zhan, S. Yan, L. Cao, and R. Ji, "Univst: A unified framework for training-free localized video style transfer," arXiv preprint arXiv:2410. 20000844 2024.
[8] Y. Yang, Q. Song. Z. Gao, G. Wang. S. Li, and X. Zhang, "Stydeco: Unsupervised style transfer with distilling priors and semantic decoupling," arXiv preprint arXiv:2508.01215: 2025.
[9] C. Klimmt, C. Roth, I. Vermeulen, P. Vorderer, and F. S. Roth, "Forecasting the experience of future entertainment technology: "interactive storytelling" and media enjoyment," Games and Culture, 2012.
[10] Q. Wang, X. Yang, F. Feng, J. Wang, and X. Geng, "Cluster-leamgenee Inheriting adaptive clusters for vision transformers," in Conference on Neural Information Processing Systems, 2024.
[11] Q. Wang, X. Yang. H. Chen, and X. Geng, "Vision transformers as probabilistic expansion from learngene," in International Conference on Machine Learming. 2024.
[12] Y. Li, Z. Gan, Y. Shen, J. Liu, Y. Cheng, Y. Wu, L. Carin, D. Carlson, and J. Gao, "Storygan: A sequential conditional gan for story visualization." " in IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[13] A. Maharana and M. Bansal, "Integrating visuospatial, linguistic and commonsense structure into story visualization,"" " arXiv preprint arXiv:2/ 10834, 2021.
[14] Y. Zhou, D. Zhou, M.-M. Cheng, J. Feng, and Q. Hou, "Storydiffusion: Consistent self-attention for long-range image and video generation," arXiv preprint 2rXiv:2405.01434, 2024.
[15] Y. Gong. Y. Pang, X. Cun, M. Xia, Y. Hc, H. Chen, L. Wang, Y. Zhang, X. Wang, Y. Shan et al., "Talecrafter: Interactive story visualization with multiple characters," arXiv preprins 2023.
2r35vv
[16] J. Mao, X. Huang, Y. Xie, Y. Chang, M. Hui, B. Xu, and Y. Zhou, "Story-adapter: A training-free iterative framework for long story visualization," arXiv preprint arXiv:2410.06244 2024.
[17] C. Liu, H. Wu, Y. Zhong, X. Zhang, Y. Wang, and W. Xie, "Intelligent grimm-open-ended visual storytelling via latent diffusion models." " in IEEE Conference on Computer Vision and Pattern Recognition, 2024.
[18] J. Rosenfeld, Make scene: Crafting a powerful story one scene at time. Penguin, 2007.
[19] J. Hart, The Art of the Storyboard: A filnmaaer'ss 's introduction. Routledge, 2013.
[20] H. Ye, J. Zhang. S. Liu, X. Han, and W. Yang, "Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models," arXiv preprint arXiv:2308.0673 2023.
[21] M. Everaert, M. Bocchio, Arpa, S. Süsstrrunk, and Achanta, "Diffusion style," in International Conference Computer Vision, 2023.

---

[22] D. Zhou, J. Huang, J. Bai, J. Wang, H. Chen, G. Chen, X. Hu, and P.-A. Heng. "Magictailor: Component-contre controllable personalization in -oi-ate diffusion models," arXiv preprint arXiv:2410. 13370, 2024.
[23] S. Zhang, B. Xie, Z. Yan, Y. Zhang, D. Zhou, X. Chen, S. Qiu, J. Liu, G. Xie, and Z. Lu, "Trade-offs in image generation: How do different dimensions interact?" arXiv preprint arX2v:250..22/00 2025.
[24] Y. Li, H. Liu, Q. Wu, Mu, Yang, Gao, C. Li, and J. Lee, "Gligen: Open-set grounded text-to-image generation," in IEEE Conference on Computer Vision and Pattern Recognition, 2023.
[25] L. Zhang, A. Rao, and M. Agrawala, "Adding conditional control to text-to- -intae diffusion models." in International Conference on Computer Vision, 2023.
[26] C. Mou, X. Wang, Xie, Y. Wu, J. Zhang, Z. Qi, and Y. Shan, "T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models," in AAAI Conference on Artificial Intelligence, 2024.
[27] R.-C Tu, Y. Ji, J. Jiang, W. Kong, C. Cai, W. Zhao, H. Wang. Y. Yang, and W. Liu, "Global and local semantic completion learning for vision-language pre-training," IEEE Transactions Puttern Analysis and Machine Intelligence, 2025.
[28] R.-C. Tu, X.-L. Mao, J.-Y. Liu, Z.-A. Ma, T. Lan, and H. Huang, "Prospective layout-guided multi-modal online hashing," " IEEE Transactions on Image Processing, 2025.
[29] Lin, G. Zhao, J. Xu, G. Wang. Z. Wang, A. Dantcheva, L. Du, and Chen, "Difftv: Identity- preserved thermal-to-visible face translation via feature alignment and dual-stage conditions," in ACM International Conference on Multimedia, 2024.
[30] J. Lin, Y. Wu, 7. Wang, X. Liu, and Y. Guo, "Pair-id: A dual modal framework for identity preserving image generation," IEEE Signal Processing Letters, 2024.
[31] Z. Yang, J. Wang. Z. Gan, Li, K. Lin, Wu, N. Duan, Z. Liu, C. Liu, M. Zeng et al., "Reco: Region-controlle text-to-image generation," in IEEE Conference on Computer Vision and Puttern Recognition, 2023.
[32] J. Xie, Y. Li, Y. Huang, H. Liu, W. Zhang, Y. Zheng, and M. Z. Shou, "Boxdiff: Text-to- image synthesis with training-free box-constrained diffusion," in International Conference on Computer Vision, 2023.
[33] J. Huang, Y. Huang, J. Liu, D. Zhou, Y. Liu, and S. Chen. "Dual-schedule inversion: Training- and tuning-free inversion for real image editing," " in Winter Conference on Applications of Computer Vision 2025.
[34] J. Huang, G. Zhang, Z. Jie, S. Jiao, Y. Qian, L. Chen, Y. Wei, and L. Ma, "M4v: Multi-modal mamba for text-to-video generation," arXiv preprint arXiv:2506. 10915, 2025.
[35] G. Meng, J. Huang, Y. Wang, Z. Fu, X. Ding, and Y. Huang, "Progressive high-freque figh-frequency reconstruction for pan-sharpening with implicit neural representation." " in AAAI Conference on Artificial Intelligence, 2024.
[36] G. Meng J. Huang, J. Tu, Y. Wang, Y. Lin, X. Tu, Y. Huang, and X. Ding, "Accelerated diffusion via high-low frequency decomposition for sun-sharpening," " in AAAI Conference on Artificial Intelligence, 2025.
[37] G. Meng, J. Tu, J. Huang, Y. Lin, Y. Wang, X. Tu, Y. Huang, and X. Ding, "Sp3ctralmamba: Physics-driven joint state space model for hyperspectral image reconstruction," in AAAI Confer- ence on Artificial Intelligence, 2025.
[38] S. Chen, Bai, Z. Zhao, T. Ye, Q. Shi, D. Zhou, W. Chai, X. Lin, J. Wu, C. Tang et al., "An empirical study of gpt-4o image generation capabilities," arXiv preprint arXiv:2504.05977 2025.
[39] Q Song, X. Wang, D. Zhou, J. Lin, C. Chen, Y. Ma, and X. Li, "Hero: Hierarchical extrapolation and refresh for efficient world models," arXiv preprint arXiv:2508. 2025.
[40] Huang, M. Yan, S. Chen, Huang, and S. Chen, "Magicfight: Personalized martial arts combat video generation," in ACM International Conference on Multimedia, 2024.
12

---

[41] Y. Lin, Z. Lin, K. Lin, J. Bai, P. Pan, C. Li, H. Chen, Z. Wang, X. Ding, W. et al., "Jarvisart: Liberating human artistic creativity via an intelligent photo retouching agent," arXiv preprint arXiv:2506. 17112, 2025.
[42] Y. Lin, Z. Lin, H. Chen, P. Pan, C. Li. S. Chen, K. Wen, Y. Jin, W. Li, and X. Ding, "Jarvisir: El- evating autonomous driving perception with intelligent image restoration," in IEEE Conference on Computer Vision and Pattern Recognition, 2025.
[43] C. Li, C. Zhang, W. Xu, Lin, J. Xie. W. Feng. Peng, Chen, and W. Xing, "Latentsyne: Taming audio-condittoned latent diffusion models for lip syne with syncnet supervision." arXiv preprint arXiv:2412.099: 09262, 2024.
[44] X. Pan, P. Qin, Li, H. Xue, and W. Chen, "Synthesizing coherent story with auto-regressive latent diffusion models," " in Winter Conference on Applications of Computer Vision, 2024.
[45] T. Rahman, H.-Y. Lee, J. Ren, S. Tulyakov, S. Mahajan, and L. Sigal, "Make-a-story: Visual memory conditioned consistent story generation," in IEEE Conference on Computer Vision and Puttern Recognition, 2023.
[46] X. Shen and M. Elhoseiny, "Large language models as consistent story visualizers," arXiv preprint arXiv:2312.02252, 2023.
[47] S. Yang. Y. Ge, Y. Li, Y. Chen, Y. Ge, Y. Shan, and Y. Chen, "Seed-story: Multimodal long story generation with large language model," arXiv preprint arXiv:2407.08688 2024.
[48] O. Avrahami, A. Hertz, Y. Vinker, M. Arar, S. Fruchter, O. Fried, D. Cohen-Or, and D. Lischinski. "The chosen one: Consistent characters in text-to-image diffusion models," in ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques, 2024.
[49] J. Zhao, H. Zheng, C. Wang, L. Lan, W. Hunag, and Y. Tang, "Magicnaming: Consistent identity generation by finding a" name space" in 12i diffusion models," " arXiv preprint arXiv:2412.14902. 2024.
[50] Q. Wang, B. Li, X. Li, B. Cao, L. Ma. H. Lu, and X. Jia, "Characterfactory: Sampling consistent characters with gans for diffusion models," arXiv preprint arXiv:2404. 15677, 2024.
[51] Black Forest Labs, "Flux," https://ituub.com/blackk forest- wewe-fooosssttssttieeee labs/flux 2023.
[52] N. Kumari, B. Zhang, R. Zhang, E. Shechtman, and J.-Y. Zhu, "Multi-concept customization of text-to-image diffusion," in IEEE Conference Computer Vision and Pattern Recognition, 2023.
[53] Wang, S. Bai, S. Tan, S. Wang. Z. Fan, J. Bai, K. Chen, X. Liu, J. Wang, W. Ge al., "Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution," arXiv preprint arXiv:2409. 12191, 2024.
[54] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, "High-resolution image synthesis with latent diffusion models," in IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[55] J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al., "Gpt-4 technical report," arXiv preprint 2xXiv23333008744 2023.
[56] R. Gal, Y. Alaluf, Y. Atzmon, O. Patashník, A. H. Bermano, G. Chechik, and D. Cohen- Or, "An image is worth one word: Personalizing text-to-image generation using textual inversion," arXiv preprint arXiv:2208. 0/610, 2022.
[57] S. Fu, N. Tamir, S. Sundaram, L. Chai, R. Zhang, T. Dekel, and P. Isola, "Dreamsim: Learning new dimensions of human visual similarity using synthetic data," arXiv preprins arXiv:2306.09:: 003444944 2023.
[58] M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafranice, V. Khalidov, P. Fernandez, D. Haziza, Massa, A. El-Nouby al., "Dinov2: Learning robust visual features without supervision." 2023.
arXiv preprint
[59] Z. Li, M. Cao, X. Wang, Z. Qi, M.-M. Cheng, and Y. Shan, "Photomaker: Customizing realistic human photos via stacked id embedding," in IEEE Conference on Computer Vision and Pattern Recognition, 2024.
[60] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li. S. Wang, L. Wang, and W. Chen, "Lora: Low-rank adaptation of large language models," arXiv preprint arXiv:2106. 09685, 2021.
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Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy (https://neurl)) http::///eerrrp..coooooooarrrr// for what should or should not be described.
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A Implementation Details about Baselines
CustomDiffusion tuning-basee method that requires fine-tuning inject given visual concepts. Specifically, we fine-tune it using the local scene images obtained from our VLM-guided scene planning framework. During inference, we adopt the corresponding story sub-prompts together with the fine-tuned model to enable a fair comparison.
StoryDiffusion training-free method designed for subject-consistet story generation and natu- rally supports image conditioning. We directly use the local scene images and the associated story sub-prompts from the VLM-guided scene planning framework as input.
ConsiStory is also a training-free method for subject-consistent story generation. However, the official implementation does not support images input. For meaningful comparison, make minimal modifications to adapt it to our task, prepending the local scene images to the input batch and leveraging it reference through self-attention and feature infusion mechanisms. Additionally, the same story sub-prompts are also adopted accordingly.
B Implementation Details about VLM-Guided Scene Planning
We propose VLM-Guided Scene Planning to guide the VLM decomposing the user aser-provided theme into multiple local scenes and stories with specific instructions. The VLM is leveraged in two step, including conceptualizing the global scene and crafting local scenes and sub-prompts.
Conceptualizing the Global Scene. The complete prompt for conceptualizing the global scene consists of two main components, which are also detailed in Table
Instruction: This specifies the task and defines the specific theme from the prompt, help- ing the VLM perform effectively in global scene generation tasks. provides a structured way guide the model in synthesizing scenes and storylines.
Examples: This is an example that shows how the VLM works. The user prompt is filled into the provided system instruction. The model then continues the conversation based on the prompt and provides the scene description in the specified format.
Table 4: Complete prompt for conceptualizing the global scene.
Role
Content
Instruction
"You are now a scene planner tasked with using your rich imagination to design global scene based on the theme provided by the user. Your job is to describe the environment and layout of the scene as thoroughly as possible and then output it into concise description of more than 50 words. This description will be used to generate the corresponding global scene, which can include various styles such as anime, realism, art, abstract, etc. Note: You only need to output the final summary result, and it must not include any theme or character information." Theme: "(User Prompt)." " Please provide the corresponding output."
System:
User: "Snowy dreams and falling stars."
Examples
VLM: [A misty forest at dawn, bathed in soft golden light filtering through ancient trees. Delicate ferns and moss-covered rocks line winding paths, while a serene stream meanders through, reflecting the sky's pale hues. Birdsong fills the air, and gentle breezes stir the leaves, creating a peaceful, dreamlike atmosphere.]
Crafting Local Scenes and Sub-prompts. Complete prompt for crafting local scenes also includes two main components, which are also detailed in Table
Instruction:
The model is required to select four distinct local scenes (sub-scenes) from an image, ensuring minimal overlap, and providing the coordinates for each local scene. Subsequently, we mandate the creation of five unique stories for each local scene, focusing on protagonist's experience, summarized one sentence, The protagonist can be an animal human with generic pronouns. The instruction emphasizes creativity and coherence,
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ensuring captivating narratives while maintaining uniqueness. The output should include the coordinates of local scenes and the corresponding story sub-prompts for local scenes,
Examples: There is also an example that shows how to craft local scenes. The user needs to provide the generated global image, the VLM then continues the conversation based on the user prompt and provides the sub-scene description as required.
Table 5: Complete prompt for crafting local scenes and stories.
Role
Content
Instruction
Now you are story director. Please fully utilize this (024x102) image, unleash your imagination, and design suitable scenes from a spatial perspective for the theme -" while creating corresponding storylines for each scene from a temporal perspective. Spatial Design: Select distinct sub-scenes with minimal overlap. Provide top-left and bottom-right coordinates in the format: [x1, yl, x2, y2]. Temporal Storytelling: Create unique stories for each sub-scene (20 stories in total), focusing on protagonist's experience in each scene. Story Summary: Each story must reflect the protagonist's action or experience and be summarized one sentence (max 15 words). Protagonist Setup: The protagonist can be an animal or generic human (e.g.. "girl," ." "man"). Creativity and Coherence: Ensure creativity and engagement while maintaining uniqueness and narrative coherence. Output Format: Provide coordinates in [x1, yl, x2, y2] format for each sub-scene, followed by the stories for each.
System:
User: "[The global image presented in Figure 4 of the main paper.J"
Examples
VLM: "[Location of a local scene]: [18. 8. 506, 499]. I.A fox explores the meadow,
sniffing flowers under the moonlight. 2.The girl dances among the trees, feeling the magic of the night. 3.An owl perches on a branch, watching over the serene landscape. 4.A rabbit hops through the grass, seeking shelter for the night. 5.A deer grazes quietly, enjoying the peaceful evening..... .......
C Limitations and Future Work
Our SceneDecorator tailored for scene planning and ensuring consistency, showcasing notable advantages compared to existing methods. Nonetheless, several limitations remain: (i) As training- free method, the story generation capability of SceneDecorator largely depends on the underlying foundation models, such as FLUX.1, SDXL, and Qwen2- VL. Consequently, any limitations inherent in these base models can constrain the overall performance of SceneDecorator: (ii) For scene injection, adopt the IP-Adapter technique, which proves effective in general. However, it performs less reliably in out-of-distribution scenarios, such as depicting an elephant in the sky.
Regarding the first limitation, future work could explore more complex scene-oriented story gen- eration tasks, such as scene transitions and multi-scene integration. As for the second limitation, developing more effective scene control mechanisms beyond the current reliance on IP-Adapter would be a promising direction.
D Potential Negative Societal Impact
Our work is primarily designed for scene-oriented story generation within the broader domains of visual content creation. However, we explicitly acknowledge that technologies capable of generating multi-image narratives may also pose significant societal risks if misused. In particular:
Disinformation and Propaganda. The ability to generate visual narratives could be exploited to fabricate persuasive but false stories, amplifying the spread of disinformation.
Bias and Stereotypes. Unintended biases present in the input story themes could potentially reinforce harmful cultural stereotypes or discriminatory visual representations.
Inappropriate or harmful content. Without proper safeguards and regulatory oversight, the generated content might unintentionally include sensitive, violent, or inappropriate material, potentially causing psychological emotional harm to diverse audiences.
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We highlight these important concerns to encourage the responsible and ethical use of our method and emphasize the importance of developing safeguards against potential misuse.
E
Additional Results
To further validate the effectiveness and versatility of SceneDecorator, present additional qualita- tive comparisons in Figure IO and Figure[[] These examples highlight the model's ability to generate coherent and contextually appropriate scenes across diverse prompts and settings.
gidd mecktinn the aktre Rising bog sesl water tocaati ner
singing
plats wilkfower
metroaaid staroltss for sluds, heping sestebte the led soedii
Reference
Customniiflasion
ConsiStory
Story Diffasion
Ours
Figure 10: Additional qualitative comparisons. Our method effectively follows the text prompt while maintaining scene alignment.
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lelsurtig eefighng Mondcghore
ieany searshinng pessage
les ge356. Weading
afteotteers the ruina, sharing attnics
Reference Scene
CustemDifflsion
ConsiStory
StoryDiffusion
Ours
The giri listona to seething sound the streen feeling calm
bird
melstly incolied flowtes
Bing dianes the edacing celertic sondiess
bett forgges for (hod, fetling the peseence anoient spirits
Reference Scene
CustomDiifusion
ConsiStory
StoryDifflsion
Ours
Figure 11: Additional qualitative comparisons. Our method successfully follows the prompt while maintaining scene alignment.
24


Reviews:
[{"review_id": "m1DYZefMCT", "reviewer": "Reviewer_YYxn", "summary": "SceneDecorator is a training-free framework designed for scene-oriented story generation, aiming to maintain visual consistency across a series of images. It addresses the limitations of existing methods that the paper claims often focus on character consistency while neglecting the role of scenes in storytelling", "questions": "1. Could authors please provide a precise description of the modifications made to each baseline (CustomDiffusion, ConsiStory, and StoryDiffusion)?\n2. How robust is the VLM planning stage? What is the approximate failure rate (e.g., instances where the VLM provided unusable coordinates, nonsensical storylines, or failed to adhere to the theme)?\n3. Can \"SceneDecorator\" be called as   Scene-Oriented Story Generation in light of its inability to handle stories with scenes that evolve or change over time (e.g., a landscape changing from day to night, a location being altered by character actions)?", "limitations": "The authors have not adequately addressed the potential negative societal impact of their work. In the NeurIPS Paper Checklist, when asked if the paper discusses potential positive and negative societal impacts, the authors answered \"[NA]\" (Not Applicable). However, this dismissal is inadequate. A tool that improves the generation of consistent, multi-image narratives could be used to create more convincing and coherent disinformation or propaganda.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths: \n1. The Problem story consistency is a genuine problem, and the authors focus on scenes instead of just characters is a valid angle. \n2. The qualitative results in the figures are undeniably attractive.\n3. The core idea of using a powerful VLM to do the \"thinking\"—planning the global scene, then dicing it up into local scenes and storylines—is a pragmatic engineering choice\n\nWeaknesses：\n1. The “VLM-Guided Scene Planning” module is described as the core novelty of the pipeline. However, it appears to be primarily a well-crafted prompting mechanism applied to a powerful pretrained VLM (Qwen2-VL), with little algorithmic innovation. The formulation (e.g., Q = Fₜ(T)) may overstate the underlying complexity.\n2. The entire \"global-to-local\" pipeline is built on a fatal assumption: that a story's scenes are just different crops of a single, static master image. This is a profound limitation. This framework is incapable of depicting scenes that evolve or change over time, which is a core element of countless narratives.", "was_revised": true, "final_justification": "The authors have successfully addressed key concerns regarding experimental rigorand responsible research practices but their response to the system's core conceptual limitation (Q4) is no promising. \n\nOverall, I think this paper should be accepted.", "rebuttal": "We sincerely appreciate the time you dedicated to reviewing our paper and the insightful feedback you provided. \nIn the following, we would like to present our point‑by‑point responses to your comments and concerns. \n\n\n---\n\n\n> **Q1:** The “VLM-Guided Scene Planning” module is described as the core novelty of the pipeline. However, it appears to be primarily a well-crafted prompting mechanism applied to a powerful pretrained VLM (Qwen2-VL), with little algorithmic innovation. The formulation (e.g., Q = Fₜ(T)) may overstate the underlying complexity.\n\nWe appreciate the reviewer’s attention to the innovation of VLM-Guided Scene Planning. Our method indeed builds upon the pretrained Qwen2-VL model, but we would like to emphasize that our approach equips VLMs with the capability of scene planning, which they do not inherently possess.\n\nSpecifically, our innovation does not lie in modifying the model architecture itself, but in *the deliberate design of a set of strategies for achieving scene planning via VLMs*, which includes: \n\n1. **conceptualizing the global scene**, where the VLM generates a comprehensive and cohesive scene description based on a user-provided theme; \n2. **visualizing the global scene**, using an excellent T2I generator to transform these descriptions into high-quality visual representations; and\n3. **crafting local scenes and stories**, where the global scene is decomposed into multiple local sub-scenes and narratives to ensure both semantic coherence and narrative continuity. \n\nThese strategies collectively empower VLMs to undertake scene planning, transforming them from general-purpose models into specialized ones capable of bridging the gap between abstract themes and semantically coherent visual storytelling, which is a capability not previously explored in existing methods.\n\nAs mentioned above, our method adopts a three-stage process for scene planning. To precisely explain the functionality of each stage, we represent each stage as a series of formalized functions in our paper. These formulations are not intended to overstate the complexity but to explicitly clarify the distinct inputs and outputs at each stage, thereby providing a more systematic and informative illustration of the overall workflow.\n\nIn summary, our method introduces a novel way of utilizing VLMs for scene planning by designing a three-stage process that includes conceptualizing the global scene, visualizing the global scene, and crafting local scenes and stories. This combination of strategies represents a significant step forward in achieving scene-oriented story generation, exploring a technical solution that has not been studied by existing methods.\n\n\n---\n\n\n> **Q2:** Could authors please provide a precise description of the modifications made to each baseline (CustomDiffusion, ConsiStory, and StoryDiffusion)?\n\nThank you for the question regarding the implementation details of the baselines. Below, we clarify how each method is adapted to our task setting:\n\n1. **CustomDiffusion** is a tuning-based method that requires fine-tuning to inject given visual concepts. Specifically, we fine-tune the CustomDiffusion using the local scene images obtained from our VLM planning module as training data. During inference, we adopt the corresponding story sub-prompts together with the fine-tuned model to enable a comparison under our task setting.\n2. **StoryDiffusion** is a training-free method designed for subject-consistent story generation and naturally supports image conditioning. We directly use the local scene images and the associated story sub-prompts from the VLM planning module as input for comparison.\n3. **ConsiStory** is also a training-free method for subject-consistent story generation. However, the official implementation does not support images as input. For a meaningful comparison, we make minimal modifications to adapt it to our task, prepending the local scene images to the input batch and leveraging it as a reference through self-attention and feature infusion mechanisms. Additionally, the same story sub-prompts are also adopted accordingly.\n\nIt is also worth noting that all experiments are conducted on the same scene dataset processed by the VLM planning module to ensure a fair comparison.\n\nWe hope this resolves your concern, and we sincerely appreciate the opportunity to include these descriptions in our final version.\n\n\n---\n\n\n> **Q3:** How robust is the VLM planning stage? What is the approximate failure rate (e.g., instances where the VLM provided unusable coordinates, nonsensical storylines, or failed to adhere to the theme)?\n\nWe sincerely thank the reviewer for this insightful comment. Accordingly, we would like to discuss the robustness of VLM-Guided Scene Planning in two aspects: \n\n1. **Coordinate Rationality:** Failures where the VLM generates unusable coordinates are exceptionally rare. To seamlessly handle such cases, we have implemented a simple yet effective correction strategy: invalid coordinates are automatically adjusted by snapping them to the nearest valid bounding box, which ensures proper cropping.\n2. **Semantic Rationality:** To evaluate the success rate of producing semantically reasonable scenes, we conducted a quantitative assessment using GPT-4o. Specifically, for each sample, we input the story theme, the global scene, the obtained local scenes, and the story sub-prompts into GPT-4o, which is guided to conduct evaluation across three key criteria: Narrative Coherence, Theme Adherence, and Layout Reasonableness. Each criterion was scored on a 10-level scale, with scores ranging from 0 to 100. The evaluation was performed on 2,920 samples, showing that our method can also achieve satisfactory performance in the semantic aspect, with the results summarized below:\n\n| **Narrative Coherence** | **Theme Adherence** | **Layout Reasonableness** |\n| :---------------------: | :-----------------: | :-----------------------: |\n|         90.06%          |       92.57%        |          90.29%           |\n\nIn summary, our VLM-Guided Scene Planning shows strong robustness in both coordinate and semantic rationality, highlighting its effectiveness in producing coherent and reasonable scenes for visual storytelling.\n\nWe will incorporate these discussions and quantitative results into our final version to provide a more comprehensive analysis.\n\n\n---\n\n\n> **Q4:** The entire \"global-to-local\" pipeline is built on a fatal assumption: that a story's scenes are just different crops of a single, static master image. This is a profound limitation. This framework is incapable of depicting scenes that evolve or change over time, which is a core element of countless narratives. / Can \"SceneDecorator\" be called as Scene-Oriented Story Generation in light of its inability to handle stories with scenes that evolve or change over time (e.g., a landscape changing from day to night, a location being altered by character actions)?\n\nWe thank the reviewer for raising this important question regarding the temporal dynamics of scenes in our framework. In fact, our SceneDecorator is capable of handling stories with scenes that evolve or change over time, through two distinct capabilities integrated within our design:\n\n1. **Creating time-evolving story sub-prompts:** Given a local scene image, our VLM-guided scene planning module can generate a series of story sub-prompts that unfold over time within that scene (e.g., a local detail being altered by character actions), rather than producing a static story description. In this way, the VLM can infer a sequence of events based on its commonsense knowledge, such as a character arriving, interacting with the environment, and leaving, all within the same scene context. These story sub-prompts can then be used by the subsequent image generator to produce story images that change over time, achieving visual storytelling with temporal evolution.\n2. **Creating time-evolving story scenes:** Alternatively, we can also generate multiple global scene images that evolve over time (e.g., a landscape shifting from day to night) in the VLM-guided scene planing module. The VLM can then collaboratively plan and crop local scenes simultaneously from multiple global scenes to create a time-evolving storyline. The resultant local scenes naturally inherit the time-evolving visual semantics from their respective global scenes, enabling the generation of visual stories that reflect changes over time.\n\nThis flexibility allows SceneDecorator to support dynamic, time-evolving narratives, thereby addressing the mentioned limitations. Due to the policy prohibiting the presentation of images during the rebuttal phase, we will present the relevant experiments and visual results, along with these discussions, in our final version.\n\n\n---\n\n\n> **Q5:** The authors have not adequately addressed the potential negative societal impact of their work.\n\nWe sincerely appreciate your valuable feedback and apologize for initially overlooking the potential societal impacts of our work. \n\nOur work is designed for scene-oriented story generation in the domains of visual content creation. However, we recognize that any technology capable of generating multi-image narratives may carry societal risks if misused. Specifically: \n\n1. **Disinformation and propaganda**: The ability to generate visual narratives could be exploited to create persuasive but false stories, amplifying the spread of disinformation.\n2. **Bias and stereotypes**: Unintended biases in input story theme could lead to the reinforcement of harmful stereotypes or discriminatory representations.\n3. **Inappropriate or harmful content**: Without proper safeguards, generated content might unintentionally include sensitive, violent, or inappropriate material, potentially causing emotional harm to audiences.\n\nAs you suggested, we will carefully re-check and revise our checklist response and include a thorough discussion of societal impacts in our final version."}, {"review_id": "TNttMOnkdb", "reviewer": "Reviewer_pRBq", "summary": "The paper introduces a training-free framework, scenedecorator, that aims to impose coherence in scenes, with subject style diversity across different stories; essentially a scene-oriented story generation instead of character-oriented story generation widely available in the literature.", "questions": "What do the authors think about extending from scene-oriented story generation to scene-oriented movie generation? What would be the challenges and technical problems to solve?", "limitations": "Not exactly a limitation of this work, but my question would be about the next steps. From story to animation movie generation!", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Scene centric story generation and scene consistencies across subject diversity - these two aspects are the focus of the work. The authors propose to perform mask-guided scene injection and scene-sharing attentions to achieve the above mentioned goals. The qualitative examples help grasp the idea behind the work easily. \nThis is an interesting and possibly useful tool for content creators.", "was_revised": true, "final_justification": "I'm happy with accepting the paper, although I won't champion for it.", "rebuttal": "We sincerely appreciate the time you dedicated to reviewing our paper and the insightful feedback you provided. \nIn the following, we would like to present our point‑by‑point responses to your comments and concerns. \n\n\n---\n\n\n> **Q1:** What do the authors think about extending from scene-oriented story generation to scene-oriented movie generation? What would be the challenges and technical problems to solve?\n\nWe appreciate the reviewer’s forward-looking insight. Extending from scene-oriented story generation to scene-oriented movie generation is a valuable and exciting direction with significant potential to push the boundaries of AI-driven visual content creation. In the following, we discuss potential challenges and technical problems, respectively.\n\n### **Challenges**\n\nExtending from scene-oriented story generation to movie generation is a natural and promising direction, but it also introduces new technical demands. Current methods rely on image generation models to handle isolated scenes. Moving toward movie generation requires video generation models that can account for temporal dynamics and maintain consistency over time. Based on our observations, we outline three challenges worth exploring:\n\n1. **Multi-Scene Shot Transitions:** In a movie, transitions between shots and scenes also carry narrative expression. How a scene begins and ends, how camera angles shift, and how characters move across shots all affect storytelling. One key challenge is making these transitions smooth and logically consistent so that the final result doesn’t feel fragmented. This will likely require finer control over visual continuity and temporal alignment.\n2. **Scene-Expandable Movie Generation**: Generating movies where scenes can dynamically expand or evolve depending on the narrative context presents another challenge. For instance, enabling the system to transition from focused, character-driven moments to broader, environment-focused shots requires the ability to generate multi-scale content while maintaining narrative cohesion. This capability can support richer storytelling and more immersive visual experiences.\n3. **Multi-Modal Integration with Scenes**: Combining scene information with multi-modal conditions, such as synchronized audio (e.g., dialogue, ambient sounds) or textual descriptions, is key to enriching storytelling. This requires aligning visual semantics with auditory and textual elements while ensuring temporal consistency. Effective integration across modalities can greatly enhance the emotional depth and immersion of generated movies.\n\n### **Technical Problems**\n\nNaturally, scene-oriented movie generation still faces several technical limitations due to the increased complexity of generating temporally consistent, high-quality video content compared to static images. Below, we highlight two critical technical problems that require further exploration: \n\n1. **Computational Cost:**  Movie generation is inherently more resource-intensive than image generation, as it involves producing high-resolution video frames with consistent temporal dynamics. This leads to significantly higher computation, memory, and storage requirements. To address these issues, methods such as model compression and sparse computation could be explored to reduce computational overhead while ensuring scalability during training and inference.\n2. **Generation Quality:** Ensuring generation quality is crucial, as it directly impacts the audience's engagement and the overall storytelling experience. High-quality movie generation requires maintaining narrative coherence, stylistic consistency, and a balance between creativity and realism. The system must ensure that visual elements align with the storyline, follow a cohesive artistic direction, and produce compelling yet believable content. \n\nWe believe that scene-oriented movie generation will emerge as an important research area in the near future, and we hope our work can serve as a foundation for further exploration in this domain. Once again, we sincerely thank the reviewer for the insightful comments, which have inspired us to think deeply about the future directions of our research."}, {"review_id": "whpg91atnC", "reviewer": "Reviewer_pX2S", "summary": "The paper introduces SceneDecorator, a novel training-free framework for scene-oriented story generation that addresses two key challenges: (i) scene planning to ensure narrative coherence across scenes, and (ii) scene consistency to maintain coherence across multiple storylines. Unlike existing methods that focus primarily on character consistency, SceneDecorator leverages a VLM-Guided Scene Planning strategy to decompose user-provided themes into globally coherent scenes and local sub-scenes in a \"global-to-local\" manner. Additionally, it employs a Long-Term Scene-Sharing Attention mechanism to preserve scene consistency while allowing subject style diversity. The framework demonstrates superior performance in qualitative and quantitative evaluations, with applications in creative domains like film and game design.", "questions": "None", "limitations": "yes", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "### Strength\n\n1. The technical approach is rigorous, combining VLMs (Qwen2-VL) and diffusion models (SDXL) innovatively. The proposed Scene-Sharing Attention and Extrapolable Noise Blending are well-designed to address long-term consistency with low computational overhead (Table 2).\n2. The paper is well-structured, with clear motivations (e.g., limitations of independent scene generation in §1) and method descriptions (e.g., Figure 2 illustrates the pipeline effectively).\n3. Addresses an underexplored problem (scene consistency in storytelling) with practical applications in film storyboarding and game design (§5).\n\n### Weakness\n\n1. Performance is bounded by the capabilities of FLUX.1-dev and Qwen2-VL\n2. The evaluation relies on 32 users which may not capture diverse cultural interpretations of scene coherence.", "was_revised": true, "final_justification": "I have read the comments of other reviewers.\n\nAfter reviewing the responses from the authors, my concern is addressed and I think it is a good contribution for the research community.\n\nTherefore, I have updated my final rating scores.", "rebuttal": "We sincerely appreciate the time you dedicated to reviewing our paper and the insightful feedback you provided. \nIn the following, we would like to present our point‑by‑point responses to your comments and concerns. \n\n\n---\n\n\n> **Q1:** Performance is bounded by the capabilities of FLUX.1-dev and Qwen2-VL.\n\nThank you for raising this important point. While our method is  indeed built upon existing backbones, we would like to emphasize the **flexibility** of the designed paradigm. \n\nThe core contribution of our work lies in the structured VLM-based scene planning framework and the long-term scene-sharing attention mechanism. These components are inherently plug-and-play and highly flexible, enabling seamless integration with both current and future foundation models without requiring architectural modifications. As demonstrated in Sec. 5 of the main paper, our approach is also compatible with a wide range of existing techniques, including PhotoMaker, ControlNet, and stylized LoRAs.\n\nCurrently, we adopt FLUX.1-dev and Qwen2-VL as our base models, as they represent some of the state-of-the-art open-source models in their respective domains. We are confident that as advanced foundation models emerge, integrating them into our framework will further enhance performance and unlock greater potential.\n\nWe hope this clarifies your concern, and we sincerely appreciate the opportunity to address this point in our final version.\n\n\n---\n\n\n> **Q2:** The evaluation relies on 32 users which may not capture diverse cultural interpretations of scene coherence.\n\nWe appreciate your valuable feedback and agree that broader and more diverse participants are important for evaluating cultural interpretations of generated results. \n\nTo address this concern, we have expanded our user study to include an extended participant pool. Specifically, we conduct an extended evaluation involving participants from a range of countries, cultural backgrounds, genders, and age groups. This updated user study yields 61 valid responses, which we believe offer a more representative and comprehensive view of how Text Alignment, Scene Alignment, and Image Quality are perceived across different demographics. The results are summarized below:\n\n| **Methods**           | **Text Alignment** | **Scene Alignment** | **Image Quality** |\n| :-------------------- | :----------------: | :-----------------: | :---------------: |\n| CustomDiffusion       |        7.9%        |        3.4%         |       6.0%        |\n| Consistory            |       21.3%        |        14.1%        |       24.7%       |\n| StoryDiffusion        |       14.3%        |        6.3%         |       11.8%       |\n| SceneDecorator (Ours) |     **56.5%**      |      **76.2%**      |     **57.5%**     |\n\nThese results show that our method, SceneDecorator, consistently outperforms existing baselines such as CustomDiffusion, ConsiStory, and StoryDiffusion, even when evaluated by participants from diverse cultural backgrounds. This superior performance further highlights the robustness and generalizability of our framework across cross-cultural users.\n\nWe hope this expanded analysis helps address your concern, and the extended user study and its findings will be included in our final version."}, {"review_id": "IlRRVi2AF3", "reviewer": "Reviewer_ohEf", "summary": "This paper proposes SceneDecorator, which can decorate a patch of the input image with story prompts generated by VLM and output the decorated images with additional contents. The method leverages diffusion models with masks for the generation. The experiment has qualitative and quantitative results, which try to prove the effectiveness of this proposed method.", "questions": "If the authors could answer my question in the weaknesses section, and solve the generation consistency problem, and ensure that it's truly a decoration, not a re-creation, I would raise my points to positive.", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. the paper is well written and easy to follow.\n2. the evaluation is complete with qualititative metrics (automatic and human based), quantitative images, ablation study, and memory efficiency analysis.\n\nWeaknesses:\nthe main concern is that, the paper claims as a scene decorator, but the generation results are so different in terms of the background. It somehow totally change the background sturcture and does a re-creation from the input patches. My point is that, the generation consistency is not that good. and it's caused by the method design.", "was_revised": true, "final_justification": "After discussion with the author, I believe that SceneDecorator failed to generate a consistent story with a complete storyline, although the authors claim so in the rebuttal and responses. It is essentially a text conditioned image 2 image generation. That's why I tend to borderline reject this paper.", "rebuttal": "We sincerely appreciate the time you dedicated to reviewing our paper and the insightful feedback you provided. \nIn the following, we would like to present our point‑by‑point responses to your comments and concerns. \n\n\n---\n\n\n> **Q1:** The main concern is that, the paper claims as a scene decorator, but the generation results are so different in terms of the background. It somehow totally change the background sturcture and does a re-creation from the input patches. My point is that, the generation consistency is not that good and it's caused by the method design. \n\nWe appreciate the reviewer’s insightful concern and understand the concern regarding the changes in background observed in the generated results.\nBelow, we would like to provide a clarification regarding our task setting.\n\nThe task we study, scene-oriented story generation, aims to *maintain high-level scene consistency*, which involves preserving the environment, visual style, and overall atmosphere of a scene, rather than reproducing scenes at the pixel level. *Such a task definition aligns with the widely recognized objective of mainstream story generation*, allowing reasonable scene changes for contextual adjustments and adaptations to better fit the storylines, since scenes are not expected to remain completely static as the story changes.\n\nIn other words, the essence of scene-oriented story generation lies in generating coherent and engaging story images, where variations in the scene are inevitably introduced to adapt to storytelling content. In this sense, our SceneDecorator is designed to function not as a low-level visual detail preserver but as a high-level visual semantic transmitter.\n\nIf strict low-level visual preservation were enforced, the task would devolve into image editing or inpainting, which diverges from the objective of mainstream story generation.  In contrast, scene-oriented story generation preserves the core identity of the scene while allowing for controlled creative flexibility, which we believe is more aligned with real-world applications.\n\nFurthermore, we have conducted extensive experiments demonstrating that our approach achieves state-of-the-art performance, as validated across three aspects of evaluation: quantitative metrics, qualitative analysis, and user studies. These results showcase the effectiveness of our method in balancing scene coherence and adaptability in the context of story generation.\n\nWe sincerely thank the reviewer for raising this critical point, and we hope this clarification addresses the concern."}]

Author rebuttal:

Meta-review: Proposed approach is designed to generate multi-image stories with consistent background and context. The reviews for the paper are largely positive with 1 x Borderline Accept, 1 x Accept, 1 x Strong Accept and 1 x Borderline Reject. Reviewers raised a number of cenrns regarding the work, including (1) with assumptions that story scenes are different crops of the single static master image [YYxn], (2) bounds on performance that are underlined by FLUX.1-dev and Qwen2-V [pX2S] and (3) lack of generation consistency in the results [ohEf].Authors have provided responses to these concerns. In general reviewers were convinced with the responses with exception of [ohEf], who remains concerned about consistency of characters among the panels of the story. 

AC has read the reviews, rebuttal and the discussion that followed. AC agrees with [ohEf] that there is a lack of consistency with respect to characters. However, AC also agrees with authors that the focus of the paper is on the scene consistency, where indeed the proposed approach excels. Ideally, it would be nice to see both, which AC believes would be possible if scene guidance (proposed) was to be combined with character guidance (prior work) for a fully consistent result. Unfortunately, the paper falls short of this. At the same time, the proposed approach is still valuable and likely to motivate future work where scene and character consistency are both preserved. On these grounds AC is recommending Acceptance.
Decision: Accept (poster)