PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework
Authors: Sixiang Chen
Jianyu Lai
Jialin Gao
Tian Ye
Haoyu Chen
Hengyu Shi
Shitong Shao
Yunlong Lin
Song Fei
Zhaohu Xing
Yeying Jin
Junfeng Luo
Xiaoming Wei
Lei Zhu
Venue: ICLR 2026 Poster (2026)
Keywords: Aesthetic Poster Generation
Unified Framework
Specific Large-scale Data
Primary area: generative models

Abstract: Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of abstract artistic content, striking layouts, and overall stylistic harmony. To address this, we propose PosterCraft, a unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore coherent, visually compelling compositions. PosterCraft employs a carefully designed, cascaded workflow to optimize the generation of high-aesthetic posters: (i) large-scale text-rendering optimization on our newly introduced Text-Render-2M dataset; (ii) region-aware supervised finetuning on HQ-Poster-100K; (iii) aesthetic-text reinforcement learning via best-of-n preference optimization; and (iv) joint vision–language feedback refinement. Each stage is supported by a fully automated data-construction pipeline tailored to its specific needs, enabling robust training without complex architectural modifications. Evaluated on multiple experiments, PosterCraft significantly outperforms open-source baselines in rendering accuracy, layout coherence, and overall visual appeal—approaching the quality of SOTA commercial systems.

Published as a conference paper at ICLR 2026
POSTERCRAFT: RETHINKING HIGH-QUALITY AES- THETIC POSTER GENERATION IN A UNIFIED FRAME- WORK
Sixiang Chen 1:2 Chen'.2;Jaanyu Lai'; Jialin Gao"; Tian Yc', Haoyu Chen', Hengyu Shi', Shitong Shao', Yunlong Lin Song Feit, Zhaohu Xing', Yeying Jin Junfeng Luo?, Xiaoming Wei Lei Zhu
1 The Hong Kong University of Science and Technology (Guangzhou)
Meituun
3 Xiamen University
"National University of Singapore
Thhe Hong Kong University of Science and Technology
ABSTRACT
Generating aesthetic posters is more challenging than simple design images: it requires not only precise text rendering but also the seamless integration of ab- stract artistic content, striking layouts, and overall stylistic harmony. To address this, propose PosterCraft, unified framework that abandons prior modular pipelines and rigid, predefined layouts, allowing the model to freely explore co- herent, visually compelling compositions. PosterCraft employs a carefully de- signed, cascaded workflow to optimize the generation of high-aesthetic posters: large-scale text-rendering optimization on our newly introduced Text-Render- HQ-Poster-100K, (iii)
2M dataset; (ii) region- supervised fine-tuning aesthetic-text reinforcement learning via best-of-n preference optimization; and (iv) joint vision-language feedback refinement. Each stage is supported by fully automated data-construcctin pipeline tailored to its specific needs, enabling ro- bust training without complex architectural modifications. Evaluated multiple experiments, PosterCraft significantly outperforms open-source baselines in ren- dering accuracy, layout coherence, and overall visual appeal approcchpoo the quality of SOTA commercial systems.
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Figure I: Aesthetic posters generated by PosterCraft demonstrate that backgrounds, layouts, and typographic designs are produced directly from textual input without modular designs, highlighting its unified framework for visually consistent and appealing designs.
"Equal contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
INTRODUCTION
Despite recent advances in automated visual design, aesthetic poster generation remains formidable challenge and is still relatively underexplored. Existing generative approaches primarily focus on foundational tasks such as text rendering Chen et al. (2023b); Liu et al. (2024); Tuo et al.
2202);; Chen et al. (2023a) or the creation of specific product-oriented posters Wang et al. (2025);
Gao et al. (2025); Podell et al. 2023), offering limited capacity to produce high-quality, aestheti- cally compelling outputs. These methods often fall short of addressing the multifaceted demands of aesthetic poster design, which requires not only (i) accurate and stylistically coherent text, but also (ii) the creation of abstract and visually appealing artistic content and (iit) the striking layouts and holistic stylistic consistency. Therefore, aesthetic poster generation demands a more comprehensive synthesis of content, form, and communicative intent.
Recent approaches to aesthetic poster generation Chen et al. (025a): Yang et al. (0024b); Seol al. 2024); [Tang et al. (2023) have primarily followed a modular design paradigm. Typically, fine-tuned vision- laigunlanguage model (VLM) acts as layout planner, suggesting text content and posi- tioning. The suggestions are then overlaid onto a separately generated background, or used as hard constraints for the generative model to follow. However, this design strategy presents several limita- tions. (i) Lack of aesthetic consistency: it undermines the visual and stylistic coherence essential for aesthetic poster creation. (ii) Limited visual quality: it constrains the upper bound of visual quality due to the decoupled design process and heavy reliance on the VLM's accuracy and robustness. In contrast, existing end-to-end design-centered generation approaches Chen et al. (2023b); [Inoue et al.) (2024): (oagg et al. ) Gao et al. (2025) remain limited to relatively simple tasks, such as greet- ing cards product compositions, which lack the visual and structural complexity of high-quality aesthetic posters. Additionally, while powerful foundation models
http:///tuuu..oomllaakkoooooo labs/flux (2024): Esser al.] (2024); Al (2024) have demonstrated impressive capabilities in gen- erating complex natural images, they still fall short of meeting all the specific requirements of aes- thetic posters. (e.g. precise text rendering, abstract artistic content, and holistic stylistic coherence). For this, we classify it as (iii) Simplified use cases. More importantly, the absence of large-scale, versatile datasets tailored specifically for aesthetic poster generation has further constrained the de- velopment of fully generative solutions (iv) Absence of targeted datasets
To move beyond the limitations of modular and simply scoped designs, we leverage foundation mod- els to explore unified generation for aesthetic posters, aiming for visually coherent and compelling results. We argue that incremental, component-level improvements are insufficient for major aes- thetic gains. Instead, propose unified framework, PosterCraft, which includes comprehensive workflow to systematically perform four critical stages: (i) scalable text rendering optimization, (ūi) high-quality poster fine-tuning, (iii) aesthetic-text reinforcement learning, and (iv) vision-languas feedback refinement. We construct specialized datasets for each stage through automated pipelines, enabling robust training and future research in aesthetic poster generation. This framework empow- ers the trained model to generate high-quality posters end-to-end. Experiments show our approach outperforms existing baselines and is competitive with several closed-source models.
Overall, our contributions can be summarized as follows:
A unified framework for aesthetic poster generation: We revisit aesthetic poster genera- tion through an end-to-end approach for high-quality, visually coherent posters, surpassing prior modular pipelines and methods focused on simpler or preduct-centric designs.
We propose a unified training pipeline with four stages: (i) scalable text rendering optimization, (ii) high-quality poster fine-tuning, (iii) aesthetic-text reinforcement learning, and (iv) vision- vision-language feedback refinement. Each stage targets key challenge in aesthetic poster generation, enabling the model to produce artistically compelling results at inference time.
A cascade workflow for high-quality poster optimization:
Stage-specific, fully automated dataset construction: We construct specialized datasets for each workflow stage using automated collection and filtering, tailored to the unique de- mands of aesthetic poster generation. These datasets overcome the limitations of resources and support more robust, transferable training.
Superior performance over existing baselines: Extensive experiments show that our method significantly outperforms open-source baselines in terms of aesthetic quality and layout structure, and achieves competitive performance compared to commercial systems.
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RELATED WORKS
Image Generation for Design Images. Diffusion models have rapidly matured across a wide range of vision problems beyond generic text-to-image synthesis Chen et al. (2024b); Lin al. (2025):
Fei et al. 2025); Chen et al. [2023c] 2025c); Li et al. (2023). Meanwhile, for high-fidelity visual synthesis, diffusion/llow matching families remain the dominant backbone in recent systems Chen Esser et al. (2024); Geng et al. (2025) httpp::/githhb..cmm. wttplluu.oooombaabaoooe
ct al. (2026); Esser
labs/flux (2024);
Wu et al. (2025); Ye et al. (2025). Recently, design-centre image generation has transitioned from early GAN- and VAE-based generators to more powerful diffusion-based frameworks CChen et al.
2202b); Tuo al. (2023); Wang et (2025); Gao et (2025); Inoue et al. (2023); Zheng et al. 2023); Chen et al. (2023a). Notably, LayoutDiffusion Zheng et al. (2023) reformulated layout generation discrete token denoising process, achieving significant gains. Advanced pipelines like TextDiffuser Chen et al. (2023bla a) adopted two-stage approach: transformer- based layout planner followed by a diffusion model conditioned on -CR-derived masks generate coherent text images from prompts. DesignDiffussie Wang et al. (2025) improved text-centric design by enhancing prompts with character-level embeddings and applying localization loss to boost text rendering accuracy. Despite recent progress, many methods imposed rigid pre-layout constraints text and layout, often undermining overall aesthetic coherence. Furthermore, their focus on specific domains such as product ads greeting cards also limited the complexity and creativity of the tasks they address. contrast, our approach adopts a unified framework that integrates text rendering, artistic content, and layout design within single inference process.
VLM for Image Generation. Vision Assion-language models served dual roles as both high-level planners Lin et al. 2023): Luo et al. (2024): Feng al. (0233) Yang al.] (2024b a): Chen al. 2025a):

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Figure 3: The PosterCraft pipeline has four stages: (I) Text Rendering Optimization to improve text aceuracy and fidelity; (2) High-Quality Poster Fine-Tuning with region- calibration to poster styling across text and non-text regions; (3) kesthetic-Text Reinforcement Learning to instill detailed aesthetic and content preferences: and (4) Joint Vision-Language Feedback, integrating multimodal reflections for refined outputs. At inference, the fine-tuned model generates high-qualit, aesthetic posters end-to-end from a single prompt, with an optional VLM-driven critique loop.
Tang et al. (003) and fully end-to-end generators Zhou al. 2024a): Xie et al. (2024): Team
(2024a); Ma et al. (2024); Zhou et al. 30 Wu et al. (2024). Fine- adaptations PosterL-
lama Scol et al. (2024) and LasterLLava Yang et al. (2024b) employed HTML- or JSON-formatted
tokens to produce content-aware graphic designs. POSTA Chen et al. (2025)) further leveraged
a fine-tuned VLM as an "aesthetic designer", applying modular design atop existing high-quality images. Unified transformer architectures like TransFusion Zhou et al. (2024a), and JanusFlow Ma et al. (2024) advanced this paradigm by generating image and text tokens in one architecture, enabling one-shot synthesis of complete visual compositions. Complementing these approaches, feedback-driven pipelines [i al. (0024) (025) incorporated VLM- critics to assess object accuracy during training. Nevertheless, these VLM-driven methods continued to struggle with main- taining cohesive aesthetic consistency, and faced an upper bound in layout complexity imposed by the models' architectural capacity and the scope of their limited training data.
3 UNIFIED WORKFLOW AND SPECIFIC DATASET
In this work, we rethink aesthetic poster generation: a high-capacity model optimized through a unified workflow can directly produce high-quality, fully rendered posters without modular design. This allows holistic integration of text, visuals, and layout, while leveraging languae feed- back during inference for greater coherence and aesthetic appeal. Unlike prior methods that rely on layout embeddings Zheng et al. (2023); ; Gao el al. (0025); Chen et al. (2023b) or external VLM- based designers Chen al. (2025a); Yang al. (024b); Scol al. (2024) (i.e. inherently restrict a model's expressive freedom), we unlock the potential of a standard diffusion backbone via work- flow optimization rather than intricate architectural modifications. Our paradigm remains compati- ble with existing techniques and provides a flexible foundation for future advances. Fig] shows our unified optimization workflow, and Fig 2 the dataset pipeline supporting each stage.
3.1 SCALABLE TEXT RENDERING OPTIMIZATION
In the first stage of our workflow, we target the challenge of accurate text rendering, a persistent bottleneck in poster generation. Progress is hindered by two factors: (i) the scarcity of large-scale, high-quality datasets with perfectly rendered text, and (ii) most available text datasets feature plain or low-quality backgrounds, which easily make the model lose the ability to represent common backgrounds. To overcome these issues, we construct Rext-Render-2M via an automated pipeline, producing 2 million samples with diverse text (varying in content, size, count, placement, and ro- tation) rendered crisply onto high-quality backgrounds. Each text instance is paired with precise captions merged seamlessly with existing image captions. This dataset ensures both 100% text
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poster typography, plain-text scenes, and long-
rendering accuracy and rich background diversity, enhancing fidelity and robustness in real-world scenarios. Fig 2 illustrates Text-Render-2M, with construction details and examples provided in the supplementary. We then fine-tune foundation models on paired Text-Render-2M using the flow matching loss Esser et al. (2024) to enhance text rendering:
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where xy = +orS follows the forward noising schedule, 2, is its time derivative, and Us predicts the velocity field. Discretized over timesteps, this loss encourages the model match the true data flow and yields markedly improved text rendering. As shown in Fig 4 the model augmented with our text-rendering optimization achieves significant gains in both rendering accuracy and text alignment across poster typography, plain-text scenes, and long-text posters.
3.2 HIGHI-QUAIIY POSTER FINE-TUNING
HQ-Poster-100K. To build high-quality dataset for supervised fine-tuning, we introduce HQ- Poster- 100K, a carefully filtered poster dataset. The pipeline first removes exact duplicates via MD5 hashing. To exclude posters with large credit/billing blocks, employ MLLM-basse scoring system that asks a binary question about their presence, outputs logits, and applies Softmax to obtain probabilistic scores. Scores closer I indicate better alignment with criteria:
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Where set T. contains all the option letters, 1, represents the logit for option x. For two-choice questions (A, BB, we set W 0 and wy 1. In our pipeline, logits computed using VLM Intern VL Team (2024h) with 0.98 threshold to control filtering stringency. We then apply perceptual hashing remove visually similar posters. The remaining posters are cap- tioned by Gemini2.5-Flan and scored with HPS, filtering out those below 0.25 to ensure aesthetic alignment with human preferences.
VL2.5-8LL
To support Region-aware Calibration, HQ-Poster- 100K provides precise text region masks for each poster. Since traditional OCR struggles with artistic typography, we use Demini2.5-Fllas extract
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text region coordinates. Masks then classified major minor based their relative size, denoting large and small text areas respectively, as shown 2
Region-aware Calibration. In poster design, harmony between text and background is crucial. Since our first stage has already improved the text rendering capability the model, this phase shifts the focus overall poster style. Therefore, it. Specifically, essential text carries the core message and assigned moderate weight to ensure clarity and integration with the background; by contrast, small text-occu minimal space and prone to rendering errors-is downweighted to prevent distracting collapse. Non-text regions, which define the visual style of the poster, receive full emphasis guarantee smooth transition from high- quality imagery to unified aesthetic layout. This balanced weighting strategy allows the fine-tuned model to preserve text accuracy while strengthening the artistic integrity of the poster.
propose Region-aware Calibration achieve
We implement this via a empirical per-pixel weight map w()):
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where "O" denotes point-wise multiplication by the weight map w. Different from previous scalable text rendering optimization, here multiply the scaling factor w, which encourages the model to learn both crisp text information and a cohesive aesthetic style.
3.3 ARSTHETIC-TEES REINFORCEMENT LEARNING
Poster-Preference-10006 To enhance poster aesthetics and text rendering, we construct the Poster- Preference-100K dataset. Using about 20K prompts, generate 5 images per prompt with the model after Region-aware calibration, producing 100K posters as the basis for preference pairs. With HPSv2 Wu et al. 2023). score each group of and select the highest- and lowest-scoring posters as preferred and rejected samples. Since HPSv2 evaluates only content and aesthetics, we use Gemini2.5-Flash out inconsistent pairs. This yields 6K preference pairs meeting two criteria: (i) the HPSv2 score gap 00002,, and (ii) complete text accuracy preferred posters.
verify text accuracy and style consistency in the preferred posters, filtering
Testhetic-Text Preference Optimization. While earlier stages ensure pixel-level text fidelity and calibrated styles, they miss higher-order trade-offs that make a poster genuinely compelling: In par- ticular, (i) detailed preferences, such as subtle layout balance, color harmony, and typographic cohe- sion, which require global evaluation beyond per- per-pixel accuracy; (ii) even after achieving crisp text rendering, further corrective tuning necessary to alleviate residual errors and seamlessly integrate text with the holistic aesthetic. To address these gaps, we frame poster generation as a reinforcement learning problem in this stage: the model must not only denoise accurately but also preferentially generate outputs that satisfy holistic aesthetic criteria.
Concretely, for each prompt, we sample n poster variants [o under the current di diffusion policy and collapse them into single "winning" and "losing" pair via best-of-n selection under the combined aesthetic-text reward R(x):
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where Pref denotes the fixed reference distribution, and pa is learned diffusion policy parameterized by 0. Because the marginal pa e) is intractable, employ the EI BB over the full diffusion

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Table A text quality comparison with SOTA poster generation models, demonstrates that Poster- Craft achieves superior performance across recall, F-score, and accuracy, while only slightly below
the recent closed-souree
O--laah--enn We highlight the best and second metries. Open
and Close denote open- seen-souree and closed-souse -ouuce.
Method
Text Recall 1 Text F-score 1
Text Accuracy
OpenCOLE O Inoue et al. (2024) (Onen)
0.082
0.076
0.061
Playground- Yang ct al. 2024b
(Open)
0.157
0.146
0.132
PosterMaker Gao ot sl. 2025 (Ope)
0.522
0.488
0.467
BizGen Peng et al 2025
(Open)
0,689
0,661
0.641
SD3.5 20244 (Open)
0,565
0,542
0,497
Flux Fluuxldev wtw.lldeellllls/eeeee httpp////gihuuu.oo/baaaee--iiei wwwpsl/oooooobboboreeeeoe labs/llux
2024
(Open)
0.723
0.707
0.667
Ideogram-VZ/22, https//ideogram. ai/launch
2024 (Close)
0.711
0,685
0.680
BAGEL Deng el al. 2025 (Open)
0,543
0.536
0,463
Gemini2.0-Flash--G... Team al.
2023
(Close)
0.798
0.786
0.746
PosterCraft (ours)
0.787
0.774
0.735
chain evaluate these log-ratio rewards, following prior work Wallace al. (2024); Wang et al. (202)_ In this way, best-of-n Aesthetic-Text Preference Optimization directly injects a unified preference signal into the diffusion training process.
3.4 VISION-LANGUAGE FEEDBACK REFINEMENT.
Roster-Reflect-120K To address potential deficiencies in content and aesthetic quality of initially posters, we apply reflection optimization to improve accuracy and quality. We build the Poster- Reflect-120K dataset by generating six posters per prompt with our preference-leamed model, to- taling 120K images. Gemini2.5-Fii target, required to meet three conditions: accurate prompt alignment, superior aesthetics, and correct text rendering. This process yields reflection-pair from each set of 6 generated images.
then selects the optimal poster from each set as the feedback
During the feedback collection phase, we gather suggestions in two key areas: Poster Content Sug- gestions and Aesthetic Style Optimization Suggestions, with Gemini2.5-Flast analyzing both the target poster and the poster requiring optimization to provide comprehensive feedback. Additionally, to optimize our prompting strategy for both feedback and VLM fine-tuning, we implement specific guidelines: the model is instructed to perform internal comparisons without explicitly referencing the second reference poster, and feedback is structured as concrete editing instructions.
Reflect VLM fine-tuning. To obtain optimization feedback during inference, construct VQA samples by embedding the original caption in the prompt alongside the generated poster requiring optimization, and using saniiii25--llas--eeeraad put configuration maintains consistency between training and inference phases, excluding reference target posters in both cases and using the original prompt as the baseline for suggestions. Addition- ally, when generating feedback with Gemini2.5-Flashe we deliberately utilize only target posters as references, omitting original captions preserve model creativity.
feedback supervision. Specifically, this in-
Joint Vision-Language Conditioning, For the poster construction, iterative crttiuue-combnng visual inspection with targeted verbal feedback is essential for refining both aesthetic content and background harmony. Inspired by this, we introduce a joint lasion-language feedback loop for mul- timodal corrections a unified workflow. For each generated-ground-eee two textual reflections, fo (Poster Content Suggestions) and fs (Aesthetic Style Suggestions). Rather than appending these strings to the original degrade performance we jointly encode them via text encoder E,, yielding 6c,. E, (fc. fa), and then concatenate this with the original prompt embedding ep (with positional encodings to pre- serve order and semanties). Additionally, drawing OmniControl Tan et al. (2024), inject the image-level feedback signal Ving (encoded by VAE) directly into the conditioning branch. The resulting multimodal context is: c=(ppi Cn,si Vimg.
pair, Gemini produces
which would exceed the encoder's 's length and
where serves the conditioning input. Finally, we fine-tune the model under the conditional flow matching objective:
VL (0) Be,zo.r (120 - -2.12.
(8)
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which enables the model to iteratively refine its outputs in response structured textual reflections and semantically enriched visual feedback.
3.5 INFERENCE
During inference, PosterCraft generates a complete poster from a single user prompt. The prompt first processed by a MLLM (e.g., Qwen3-based Yang al. (2025) Magic Prompter), which en- riches the input with detailed aesthetic cues to guide layout and content generation. PosterCraft then produces the poster in a unified manner, without any additional inputs or modular designs. Addition- ally, an VLM-based critique loop can further enhance the result by providing structured multi-modal feedback. This enables iterative refinement to improve aesthetic quality and semantic alignment.
4
EXPERIMENTS
4.1 IMPLEMENTATION
For PosterCraft, we initialize from the Flux-dev https://github. https///..oooobbcooreeooee labs/flux (2024) backbone and train mixed precision. Text rendering optimization involves 300K full full-paran parameter iterations Text- Render-2M with Slafactor[Shazeer & Stemn (018) at 22- Stage 2 fine- tune-tunss 6,000 steps on HQ-Poster- using Adafactor (Ir = le-5) with per-pixel flow-matching weights 0.6, 0.2, 1.0. For reinforcement learning, sample n=5 candidates per prompt and optimize via Adam W Loshchilov & Hutter (2017) (Ir = le-4) for 1500 steps, tuning only LoRA (rank 64). In vision- vision-language feedback refinement, dual--anguage reflections Se and fa are encoded by T5 Raffel itt al. (2020), followed by LoRA (rank 128) fine-tuning under conditional flow-matching loss for 6000 steps using AdamW (Ir = lc-4). We employ Internv13-8B Intern VL Team (2023) for feedback generation, fine-tuning 2 epochs and using temperature 0 at inference. More details about the dataset can be found in the supplementary material.
4.2 QUANTITATIVE RESULTS AND COMPARISONS
We conduct a quantitative comparison of PosterCraft against seven leading models, both open-source and commercial. ing Gemini2.0-Flash-GGn TTam et al. (2023). we randomly generate 100 aesthetic poster prompts-balanced across short, medium, and long lengths-and and sample three outputs per model, yielding 300 test images. Posters are generated with OpenCOLE, Playground-v2.5, SD3.5, Flux1.dev, Ideogram-v2. BAGEL, and Gemini2.0-Flash-GG. O-Flaa--GGn. We then apply the OCR engine the SOTA VLM to each image and re- port three snccision-orienter metrics text call, text F-score, and text accuracy averaged over all 300 samples.
Model Peeforrance Comparionn (Tour Dimemidor))
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Figure 5: User study comparisons between PosterCraft and both SOTA open-source and closed-source models. It consistently outper- forms all open-source baselines and several pro- prietary systems cross multiple dimensions, with performance marginally below that of the leading closed-source model, Gemini2.00 Cemini2.0-Fllash O-Flash--Gee
As shown in Tab. D PosterCraft achieves sub- stantially higher recall and F1 scores, captur- ing a more complete character set, while its accuracy surpasses leading open-source meth- ods (Flux 1evv, SD3.5) and competitive com- mercial solutions (Ideogram-v2), The table also includes poster-specific generators BizGen and PosterMaker, for which we provide the same five representative layouts (and no product image for PosterMaker); despite their more structured inputs, PosterCraft still attains better recall, F-score, and accuracy. PosterCraft's performance closely approaches that of some mature SOTA commer- cial systems such as Seemini..0-Flah--6e
demonstrating that its text-rendering not only advances academic benchmarks but also offers near production-level competitiveness. Besides, inspired by previous works Chen et al. (2024a); Esser et al. (2024), we conduct the user study to assess model quality. Twenty experienced poster designers evaluate outputs across multiple dimensions, as shown in Fig. 5 Experiments show that our method significantly enhances aesthetics and text rendering over the base model Flux 1. dev, and outperforms all state-of-the-art open-source and several closed-
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Figure 6: Visual comparison of different model outputs. Red boxes highlight misspelled or dis-
torted text, while yellow boxes indicate redundant or missing text elements. Within the prompts, orange text denotes content and style requirements, and red text indicates textual elements. From the visual comparison, it is evident that method achieves superior aesthetic appeal compared to existing state-of-the-art approaches. In terms of text presentation, our rendered fonts blend more content, and text rendering errors nearly eliminated.
naturally with the
source models across all evaluation metrics. Additionally, PosterCraft performs only slightly below Gemini2.0 Seiini..--FFaat O-F00F-------- Gen, validating the effectiveness of our unified workflow in fully unlocking the generative potential of the powerful baseline model. More detailed evaluation strategies and experi- ments on other benchmarks can found the supplementary material.
4.3 QUALITATIVE RESULTS AND COMPARISONS
To further evaluate the superiority of vision, conduct comprehensive visual comparison be- tween PosterCraft and six other models-four open-source and two commercial systems, as shown in Fig 6 Playground-v2.55 completely fails in text rendering, while SD3.5 renders titles only partially, with both showing poor aesthetics and weak prompt adherence (e.g., animated styles). Fluxl.dev and BAGEL improve title rendering but still contain textual errors and fall short aesthetics and prompt alignment, missing details such as "One golden mechanical owl perches on her shoulder." " Among proprictary models, Ideogram-v2 and Gemini2.0-Flasse Gen show only minor text errors in small text and achieve superior aesthetics, but both have prompt adherence issues: Ideogram-v2 mis- represents mountain formations, Gemini lacks photorealism, and neither generates silhouette effects the third image. More comparisons appear in the supplementary material.
Study Comsonarts
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Figure Abl. experiments on the core compo- nents of our workflow: Removing any of these four improvements results in sustained declines in OCR accuracy (purple) and human preference (peach), showing our design and optimizations are effective and our motivation valid.
ABLATION STUDY
In this section, we validate workflow by in- dependently assessing each stage, keeping all parameters and conditions identical to the pre- ceding experiments. Text Rendering Opti- mization is critical for both accuracy and

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perception. Text Rendering Optimization ensures clarity and fidelity. Its removal leads to drops in both OCR accuracy and human preference, confirming that large-scale, high-quality text data signif- icantly improves text rendering. The diverse and realistic backgrounds also preserve visual quality, while models without this optimization often fail to maintain legibility and accuracy, as shown in Fig and Fig. 7 Region-aware Calibration improves poster consistency. Fig- 7 demonstrates that it helps the model adapt to spatial context and balance text-background. Without it, all re- gions are treated equally, weakening stylistic coherence in visually complex posters while suffering text bias. Aesthetic-Tex, Text Reinforcement Learning and Reflection refine outputs with vision- language feedback. As shown in Fig. 7 Aesthetic- RL directly enhances the harmony between aesthetic appeal and textual accuracy, while Reflection further improves overall quality through it- erative vision-language guidance. We provide more detailed ablation studies the supplementary material.
6 CONCLUSION
PosterCraft presents a unified, cascaded workflow that integrates scalable text-rendering optimiza- tion. poster fine-tuning, RL-driven aesthetic enhancement, and joint vision-language feedback, demonstrating powerful foundation model can directly produce posters with both striking visuals and precise text. Our fully automated dataset pipelines support scalable, task-specific training, and we achieve substantial gains over open-source baselines, approaching leading commercial quality.
ETHICS STATEMENT
This work does not involve human subjects, personally identifiable information, or sensitive pri- vate data. All datasets used in this study were either automatically constructed through synthetic pipelines (e-g.. Text-Render-2M. Poster-Preference- 100k Poster-Reflect- 120K) or collected from publicly available poster resources (HQ-P)ster-100K) with automated filtering to ensure quality and compliance.
The HQ-Poster-1000 dataset contains materials protected by third-party copyrights. Their inclu- sion follows the principle of fair use under copyright law and intended solely for non-commercial scientific research. We do not claim copyright ownership of these materials, and users are responsi- ble for ensuring that any use complies with applicable laws and regulations. The publishers this project assume no responsibility for copyright disputes arising from use of the dataset.
Our method focuses on aesthetic poster generation and does not target manipulative, deceptive, or harmful applications. We committed to releasing datasets and models under appropriate open-source licenses to support transparency, reproducibility. and fair access for the research com- munity.
8 REPRODUCIBILITY STATEMENT
We have made substantial efforts to ensure the reproducibility of our work. The main paper and sup- plementary materials provide detailed descriptions of the proposed framework, dataset construction pipelines, training configurations, and evaluation protocols. Hyperparameters and ablation studies are explicitly documented to facilitate independent verification. With the growing availability of alternative models, many of our data filtering designs based on closed-source models can be eas- ily reproduced using state-of-the-art open-source lanian-e models. addition, we include the data processing scripts, evaluation code, and all prompts used in testing in the supplementary materials to further support transparency and reproducibility. All datasets and models used in our experiments are described with sufficient detail to ensure full reproducibility and to enable future research extensions.
Acknowledgments. This work is supported by the Guangdong Science and Technology Department (No. 20242DZX2004), the National Natural Science Foundation of China (Project 80.82333383, and Adobe Research Gifted Grant.
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A APPENDIX
This is supplementary material for PosterCraft: Rethinking High-Quality Aesthetic Poster Gener- ation in Unified Framework.
We present the following materials this supplementary material:
Sec A.
Detailed dataset construction on Text-Render-2M..
Sec A.2
More information about the automatic processing pipeline for HQ-Poster-1000 100K.
Sec
A.3
More examples and explanations of the Poster-Preference-100K
See
4
Detailed illustration of the Poster-Reflect-1200
See A.5
Gemini for OCR calculation and preference evaluator.
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Sec A.6
Discussion about reflection and instruction- brauud editing.
Sec A.7
An extra evaluation strategy comparing PosterCraft with other methods.
Sec A.8
More performance comparisons of PosterCraft and other methods on different
task benchmarks.
See A.9 A.9 work.
More key ablation experiments and effectiveness demonstrations of our frame-
Sec A. 10
Additional visual results and comparisons generated by our PosterCraft.
Sec A.11 Limitations.
Sec
A. 12
Future work.
Sec A.13
Usage of L.M..
Sec A. 14
Generalizability beyond Flux-dev: Experiments SD3.5.
Sec
A.15
Why unified PosterCraft framework works.
Sec A.16
Dataset availability and statistical analysis of our main datasets.
A.1 TEXT-RENDER-2M CONSTRUCTION PIPELINE
In this section, we provide detailed overview of the automated construction process behind Text- Render-2M. large-scale synthetic dataset designed to improve text rendering quality in the baseline model. This dataset plays critical role in the text rendering optimization stage of our workflow. By overlaying diverse textual elements onto high-resolution background images, it allows the model to learn accurate text generation while preserving its ability represent rich visual content.
Multi- Instanen Text Rendering Each image contains a variable number of independently placed text instances, typically ranging from one to three. This multi-instance setup introduces compositional complexity and better reflects natural layouts found in aesthetic posters.
Text Content Generation Text content is synthesized using a hybrid strategy:
majority of the samples are generated using template-based grammars, with phrases constructed from predefined structures filled with curated vocabulary lists.
A smaller portion uses random alphanumeric strings simulate noisy unstructured tex- tual inputs.
The generator supports rich variations punctuation, casing and structure (e.g., both single-word and short-phrase constructions), ensuring linguistic diversity.
Font Selection and Style Variability Fonts are randomly drawn from a categorized library contain- ing both standard and artistic typefaces. When multiple styles available, roughly even split enforced between classic and stylized fonts. The system filters fonts that do not support lower- case letters to avoid invalid renderings. This selection mechanism ensures visual variability while maintaining text legibility.
Layout and Placement Strategy Text is positioned using 3x3 grid-based partitioning scheme (e.g., tep-left, center, bottom-rrghtt, Before final placement, bounding box collision checks are performed. If an overlap is detected, the system will retry placement with different positions reduced font
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Pine fern robin hood
AdPuay
Maze out ebt relspon
shope
oguaplane near orchetrre
individunismm sparrow
deeei undeeshig thn gerdaam COSTODIAL SINCE PRPOSITE
TIDE
Gentts
chester Fiei
grotesuue
Figure 8: More samples are shown, which are high-quality paired samples in Rext-Render-2M.
sizes, often within 3-5 attempts per instance. This strategy enables dense yet legible arrangements while minimizing visual clutter.
Orientation and Alignment Orientation options include:
Horizontal,
Vertically rotated (rotated 90 degrees),
Vertically stacked (one character per line).
In the horizontal mode, a small proportion of texts receive random rotation, and subset of longer text fragments are automatically wrapped across multiple lines. Alignment is randomly selected among left, center, right justification.
Prompt Generation Each image is paired with structured natural language prompt. Prompts in- clude:
The text content,
Position (e.g., "bottom right"),
Orientation (e.g., "vertically stacked"),
Color category, and
Optionally (included in 50% of cases), the font style.
When multiple texts rendered, their prompts numbered and concatenated. If no text is
cessfully rendered, a fallback prompt indicating the absence of text is generated. We provide a number samples to view in Fig g
A.2 AUTOMATIC PROCESSING PIPELINE FOR HQ-POSTER-100K
For collecting high-quality poster images, we design an automated image filtering and annotation pipeline for HQ-Poster 100K utilizing three MLLMs:
(1) MLLM Scorer:
We employ Miermv12.5-8B-MPP with a binary classification task to filter out posters containing extensive Credit Blocks, Billing Blocks, "4K ultrahd" cover texts. The com- plete prompt is shown in Fig I7
for automated poster caption annotation, systematically describing the poster content, visual style, and textual elements. The complete prompt structure is illustrated in Fig 18
(2) Gemini Caption Generation: We utilize -aaiiii.----aas--eeeiiie
(3) Gemini Mask Generation: The final step in poster data collection involves generating text region robust OCR capabilities to extract text masks
masks. We employ Gemini2.5-Flan 5- Preitsss-revve-OAAe
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Potter
BRUGES
HOCTURNAL
VENOM
Figure 9: More pictures shown, which are high-quality paired samples with masks in HQ-Poster- 100K.
from posters, which are subsequently used for Region-aware Calibration. The prompt shown in T9
In addition, we provide more masks, images, prompts triplets in Fig Di to demonstrate the advantages
of our dataset.
A.3 EXPLANATIONS OF THE PREEEEEREEEEEIIOOOR
In constructing the pairs that meet aesthetic requirements and exhibit sufficient diversity. Subsequently, we employ evaluate the alignment between the Best of 5 posters and their corresponding prompts. The complete prompt is shown in Fig 20g Best of posters must be completely accurate while maximizing alignment with both the content and aesthetic style requirements specified in the prompt. We implement a binary classification sys- tem where the model assigns 0 to unqualified samples and to samples meeting all requirements. presents presents additional preference pairs, demonstrating that our pipeline built upon HPSv2 and Gemini effectively constructs preference data.
100K dataset, first utilize HPSy2 filter out poster
20 Our criteria require that text in the
A.4 ILLUSTRATION OF THE POSTER-REFLECT-I12
In constructing the Coster-Reflect. 120K dataset, we first employ preference-optimizze model to generate 6 posters per prompt, totaling 120K posters. Subsequently, our feedback collection pipeline utilizes Gemini2.55Fllase Saeash--rrviewOAke
two phases: first to select the Best of 6 posters as opti- mization targets, and second to gather direct feedback.
(1) Best of Selection:
We sequentially input six images to Gemini, which selects the optimal image based predetermined priorities, returning the index number of the best image. The complete prompt is shown in Fig 21
(2) Feedback Collection:
We input two images sequentially the first being the image requiring im- provement, and the second being the optimization target selected in phase (1). Feedback is collected across two dimensions: "Poster Content Suggestions" and "Aesthetic Style Optimization Sugges- tions". Each image pair receives 10 pieces of feedback through 5 Gemini requests. The complete
prompt is shown in Fig 22
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This poster the tares"
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Figure 10: Additional Preference Pairs in Rejected Samples, while those on the right are Preferred Samples. Orange text indicates textual content, and red text corresponds to content, style, or layout requirements.
The images the left
Aesthetic
Prompt Aligmment
PosterCraft
Sarmmi2.0-Fash--Co
PosterCraft
Gemmi2DD-lash
PostorCraft
BAGEL
PosterCraft
BAGEL
PosterCraft-
Ideogram-12
PostarCraft
Idnogram-v2
PostarCraft
Fluxt .dvv
PostarCraft
Flxxt Adv
PosterCraft
SD3.5
PosterCraft
SD3.6
PostorCraft
Playground- v2.5
PosterCraft
Playground-2255
PostorCraft
OpenCOLE
PosterCraft
OpanCOLE
0.0
0.2
0.8
1.0
0.0
0.6
Acouracy
Overall Preference
PosterCraft:
Gemini2.0 Faarii.--Fiash-coo Gen
PosterCraft
Gemmi2.0-Flashhco
PosterCraft
BAGEL
PostorCraft
BAGEL
PostorCraft
ideogram-22 v2
PostorCraft
Ideogramvv2
PosterCraft
Flxx1.der
PosterCraft
Flxt dev
PosterCraft
SD3.5
PosterCraft
SD3.5
PosterCraft
Playground-vee v2.5
PosterCraft
Playground--255
PosterCraft
OpenCOLE
PosterCraft
OpenCOLE
PisterCrat
Gemini serves as an authoritative evaluator for human preference comparisons across our method and other baselines. PosterCraft outperforms most state-of tf-th-rrtt generative models aesthetic coherence, prompt alignment, text rendering, and overall preference. achieves performance nearly on par with the leading commercial model Gemini2.00Flaahh- in text render- ing, while showing only a slight gap in other aspects.
Figure 11:
A.5 GEMINI FOR OCR CALCULATION AND PREFERENCE EVALUATOR
During the experimental phase, we employ Gemini2.5-Flan SwwFF-a-rreaeee----ae as both an OCR metric calculator and multi-dinensional preference evaluator. The robust perceptual and comprehension capabilities of Gemini2.5 F-aaash establish a solid foundation for our experimental accuracy.
(1) OCR Evaluator: Despite the significant challenges that artistic fonts in posters pose to traditional OCR algorithms, Semmini.5-FFlsh--Peee Previcw-05-20, as a state-of-the-at MLLM model, accurately extracts textual information from images. We further utilize Gemini2.5 5-aassh'' reasoning and math- ematical capabilities to directly compute and output OCR metrics. The specific prompt is shown in
Fig 23
(2)
Preference Evaluator: Gemini2.5- 5-aash demonstrates superior assessment capabilities with its reasoning abilities. We present two images side by side and instruct Gemini2.5-
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Table 2: Comparison on the our proposed dataset Geng al. (2025) using HPSv3 Ma et al. 2025). We highlight the best and second performance. The upward arrow (1) indicates that
higher values are better.
Method
HPSv3
OpenCOLE Inoue et al.
2024
(Open)
8.2280
PosterMaker Gao et al.
2025
(Open)
8,5825
Playground-v2.51 Yang ci
2024b
(Open)
8.7831
BizCien Peng et al. 2025
(Open)
9.4879
SD3.5 AT 2024 [Open)
10.3
Flux1.dev httins//gituuh..om/haeee forest labs/flux 20024
(Open)
10,4066
Ideogram-V2/ hitips//ideggran atllaunch 2024
(Close)
10.0911
HiDream-II-Fui Cal cl
2025 (Open)
10.4533
BAGgL[Deng et al. 2025
(Open)
8,9116
Gemini2.0-Flan
Team et 2023
(Close)
10.5251
PosterCraft (ours)
10.7402
Flash to select from "L", "R", or "None", representing left image superior, right image superior, or indeterminate (either both excellent or both inadequate). The specific prompt is shown in Fig 24 and Fig 25
A.6 DISCUSSION ON THE DIFFERENCES BETWEEN REFLECTION AND INSTRUCTION-BASE EDITING
While our refinement stage may appear similar to instruction- based editing, the underlying mecha- nisms and objectives differ substantially. We summarize the key distinctions as follows:
Source of instruction. In instruction-based editing, the model typically receives explicit, human-written commands describing object-level changes (e.g., "remove the chair" or "change background color"). By contrast, reflection mechanism relies offline thetic model that generates structured feedback from imperfect images. This enables multi- dimensional improvement, covering aesthetics, typography, and layout balance, rather than focusing solcly on localized object edits or simple manipulations.
Role of the original prompt. Instruction- initial text-to-image (T2I) prompt. In our design, the original T2I prompt remains central, while the reflection-derived instruction serves only as auxiliary guidance. This ensures that semantic alignment with the initial user intent is preserved, while aesthetics and overall visual quality are enhanced.
editing often discards or overrides the
Objective difference. specified edits. In contrast, reflection targets holistic enhancement- text render- ing, composition, and aesthetic appeal. In this sense, reflection is closer to an iterative self- self-improvement loop rather than shot command execution.
Instruction-bart editing aims for literal compliance with user-
A.7 MORE EVALUATION STRATEGY
Inspired by PixArt- Chen et al. (202aa), we further employ Semmini2.0-Flast as an automated eval- uator to provide strict preference comparisons across models, as illustrated in Fig. II Leveraging carefully designed evaluation prompts, Gemini consistently confirms that PosterCraft achieves supe- rior aesthetic quality, faithful prompt alignment, and accurate text rendering compared both open- source and several closed-source baselines. Although slightly behind the commercial Geminí2.0- Flash-Gee in text rendering, PosterCraft remains competitive overall, demonstrating the robustness of our unified workflow.
To further strengthen the aesthetic and prompt alignment preference evaluation, we additionally benchmarked PosterCraft against existing methods using HPSv3 on the prompts from proposed poster generation benchmark (Tab. 2). In this comparison we also include goster-oriented systems such BizGen and PosterMaker, adapting them to our text-only setting by supplying five represen- tative poster layouts and, for PosterMaker, disabling the product-image input. PosterCraft achieves the highest HPSv3 score (10.7402), surpassing both open-source and commercial baselines, with
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Table 3: Quantitative results on CVTG-2K Du et al. (2025) dataset. We highlight the best and
second performance for each metric. The upward arrow (1) indicates that higher values are better.
Model
Word Accuracy
NED f
CL.IPScore
VQAScore
FLUX.11dev http://////bbb.cooollleeeeeeeee/ labs/tlux 2024
0.4965
0.6879
0.7401
0.8886
AnyText Tuo Tuo
2025
0.1804
0.4675
0.7432
0.6935
TextDiffüserr22(hnne ct 2023b
0.2326
0.4353
0.6765
0.5627
RAG-Diffusion Chen e al 20240
0.2648
0.4498
0.6688
0.6397
3D1S /hou ab
0.3813
0,6505
0.7767
0,8684
TexiCrafter Du 2112
0.7370
0.8679
0.7868
0.9140
PosterCraft (Ours)
0.5814
0.7581
0.7493
0.9007
PosterCraft (Ours) with TextCrafter
0.7544
0.8863
0.7952
0.9241
Table Comparison on the LongText-Bench (English) dataset Geng al. (2025).
We highlight
the
best and second performance. The upward arrow (1) indicates that higher values are better.
Method
LongText-Bench-e
Jamus-Pru Chen al.
2025d
0.019
BLIP3-o Chen
0.021
Kolors 2.0 Team
4024b
0.258
PosterMaker Lw1 twihhull houl imigg input)
Gau et ul. 2025
0.315
BAGEL Dang et al. 22025
0.373
BizGien (random layouls impurt Peng al. 2025
0.478
HiDream-ll-Full Cui et al. 2025
0.543
OmniGen22 Wu el al. 2025
0.561
PL.UX.1-des 1-dee/hllss/// httl:s//gtliihh.ommbbll
labs/flux
0.607
X-Omm Geng et al. 2025
0.900
PosterCraft (Ours)
0.631
Gemini2.0-HH D-Flash-Gen ranking second (10.5251). That PosterCraft outperforms these task-specifie poster generators while using only a single text prompt further emphasizes the strength of our uni- fied backbone design. These results highlight the superiority of unified workflow in capturing human-preferred aesthetics beyond text accuracy and layout coherence. Taken together, our evalua- tion covers comprehensive spectrum-from VLM evaluators, to objective metrics of poster text accuracy and aesthetic quality- quality-demonstratinng that PosterCraft delivers outstanding performance in aesthetic poster generation across multiple per- spectives.
studies, to win-rate analysis with state-of-the-an
A.8 MORE BENCHIMARK COMPARISONS
Our primary task is poster generation, and the main manuscript we have already demonstrated that PosterCraft match closed-souree models in that domain. Here additionally report results on more general text-rendering benchmarks to show that our training workflow also brings con- sistent gains beyond aesthetic posters. On the partitioned text dataset CVTG-2K (u et al. (2025). PosterCraft achieves Word Accuracy = 0.5814, NED = 0.7581, CLIPScore = 0. 0,74,,, and VQAS- core = 0.9007. Although we do not outperform methods specifically designed for text rendering (for instance, TextCrafter, which uses attention- and partition-aware strategies), PosterCraft surpasses almost all open-source baselines (such as FLUX.I-dev, AnyText, TextDiffuser-2, RAG-Diffusion, and 3DIS). Moreover, when we apply the training-free TextCrafter optimizer on top of PosterCraft, the combined system (PosterCraft TextCrafter) further improves all four CVTG-2K metries and clearly surpasses TextCrafter itself, as shown in Table 3 This confirms that our four-stage training pipeline has already unlocked strong text-rendering capacity in the backhone and remains fully com- patible with specialized test-time plug-ins when desired. Notably, our base model FLUX, which demonstrates that our proposed training workflow yields substan- tive improvements even tasks different from poster generation. This strongly indicates that our workflow possesses robust generalization and resilience.
also significantly outperform
To further validate PosterCraft's capability in longer text generation in image contexts, evaluate it on the LongText--BB -Bnchh (English) dataset. PosterCraft attains a score of 0.631, ranking second among public models (only X-Omm is ahead with score of 0.900). Although we do not surpass
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AEGIS THE AMCIENTS
AEGIS OF THE ANCIENTS
Elana Whirey
THE MAGICAL TREEHOUSE TIME MACHINE
MACICAL REEHOOSE TIME MACHINEE
MYZSTERY NECGUS MOON MOTHS
THE M MYSTERY OF THE MISSING Moon Moths
TIME MACHIVE
DRISSRANE
Preteer Comment chandrras Ea eNcNT AGULE
UNDERWORLD
WNDERWORLD
anary tww
Eahanooite
NE Que
Harry Potter
HarlyPotter
Potter Teggtiin
FAEE cuternbor Drane plcurnrr CTIT chanscae
Figure 12: Aesthetic-text reinforcement learning (top row) and vision-language feedback (bot- tom row) qualitative comparisons. orange boxes denote the text biases. Different color fonts represent key feedback information from VLM. The top examples demonstrate that our reinforce- ment learning stage effectively improves overall aesthetic quality and partially corrects text render- ing errors after supervised fine-tuning. The bottom examples illustrate the impact of vision-language reflection, where feedback from a VLM is integrated into the generation loop. This results in no- ticeable enhancements both visual aesthetics and semantic coherence in the final poster outputs.
X-Omni, we outperform many prior methods (such as Janus-Pro. BLIP3-o, Kolors 2.0, BAGEL, HiDream-I1-Full, OmniGen2, FLUUX.1dee etc.), including the very strong HiDream-II-Fuut For poster-specific baselines BizGen and PosterMaker (needing layout), we randomly sample one of five predefined layouts per prompt and omit the product image for PosterMaker- so that they match the lesting form of Long BengeehhBennch Given that X-Omni is built on a much larger backbone and sub- stantially more text-centric data, this gap is expected and mainly reflects differences in base-model capacity and supervision rather than a limitation of PosterCraft. Yet the fact that we outperform most open models, and significantly surpass our base model, highlights the generalizability and effective- ness of our method. These additional benchmark results serve as supportive evidence, reinforcing the argument that PosterCraft is not limited to poster generation, but also has promise across broader visual text generation tasks.
A.9 MORE ABLATION EXPERIMENTS AND EPFECTIVENESS DEMONSTRATIONS
A.9.1 AESTHETIC-TEN REINFORCEMENT LEARNING AND VISION-LANGUAGE REFLECTION
Aesthetic-Text Reinforcement Learning. The reinforcement learning stage significantly boosts visual appeal and human preference. By optimizing higher-order aesthetic cues (c.g.. layout bal- ance, color harmony, typographic cohesion), it enables the model to generate posters that are more visually compelling and coherent. The top-row results in Fig. clearly show these improvements: reinforcement learning corrects text rendering errors left after supervised fine-tuning and enhances holistic preference, confirming its effectiveness.
Vanuan-L Reflection, The reflection stage integrates structured feedback from vision- language model (VLM) into the generation loop. This mechanism allows the system to iteratively refine stylistic integration, semantic alignment, and overall aesthetic appeal. The bottom-row ex- amples in Fig. 12 highlight that reflection leads to more consistent layouts and improved text- background harmony, particularly in visually complex posters.
These ablations and 7 in the manuscript demonstrate that both stages reinforcement learning and vision- llaiunne reflection-and are indispensable for unlocking the full potential of foundation dif- fusion models in end-to-end aesthetic poster generation.
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Stage-4 (Reflection)
Feedback generator
Feedback Quality (BERTScore-FI
Win rate
No (wlo Stage-4) Yes Yes
N/A
0.33
In3ernVL3 (original)
0.8641 + 0.0099 0.8957 + 0.0095
0.30
Intern VL3 (fine-tuned)
0.37
Table 5: Ablation the VLM used to produce Stage-4 feedhack. Fine-tuning the VI.M with high- quality, Nomain-specite feedback pairs improves both the quality of feedback text and downstream human preference.
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Ideogram
Flash
Figure 13: Visual comparison of different model outputs. Red boxes highlight misspelled or dis- torted text, while yellow boxes indicate redundant or missing text elements. Within the prompts, orange text denotes content and style requirements, and red text indicates textual elements. in- dicates that method significantly outperforms existing SOTA approaches in generating high- quality posters under long-prompt conditions, with notably improved prompt alignment. In terms of text rendering, our model produces fonts that align closely with the visual context of the scene, with minimal rendering errors.
A.9.2 THE NECESSITY OF FEEDBACK MODEL FINE-TUNING
Fine-tuning the VLM used for Stage-4 feedback substantially improves both the feedback text and downstream generation quality. Compared with the original VLM, our fine-tuned version yields higher feedback quality (BERTScore-F1 0.8957 + 0.0095 vs. 0.8641 +: 0.0099, +0.0316 16316 absolute, ~3.66% relative). higher BERTScore-FI indicates better semantic alignment between our gener- ated feedback and reference (high- thigh-quality) feedback: it means that the fine-tuned VLM's feedback is more semantically faithful to the gold feedback, and it increases the human win rate of gener- ated posters from 0.30 to 0.37 (+0.07 absolute, ~23.33% relative). Notably, using the original VLM hurts Stage-4 compared with removing reflection entirely (0.30 vs. 0.33), indicating that non- domain ill-formatted feedback can misguide the reflection loop. Enforcing structured schema and training the VLM on high-quality, domain-specific feedback pairs is therefore essential for sta- ble, effective reflection.
A.10 ADDITIONAL VISUAL COMPARISON AND EXAMPLES
In the long-prompt setting of Fig I3 PosterCraft demonstrates clear advantages in both prompt alignment and aesthetic quality. Our model consistently integrates complex scene descriptions with textual elements, producing posters that closely follow the narrative and stylistic cues in the prompts. Competing models often struggle with either missing or distorted text (c.g.. Playground- v2.5, SD3.5, l.dv, Gemini2.00Flash-GGen F-aash-Ge,,, or fail faithfully capture seene details (e.g..
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This poster for PluetPaa Retro Coroole footurco grophios, avoking nostalgia kuwwis a vibrant. playail, and retro- gerning ocsthetie.
Pixel Play
PIXELPLAY
Bere Cume
PIXELPLAY
POEPLEE
GARE
Thes poster for the uowe tonne
Oktoberfica
OKTOBERFEST
Oktoberfest
OKOOBERFFET
DEER CARDDNN
Oktoberfest
Beverion putions, beer staine, pretoels, and lodeiioseen preating festive cheeriut and outtuslly
Bee- Ganden
Oktoberfest
Cilication
BEER GAIRDE
Solgrs
CERLIPETITEN
This poster for the International
ANIMATION
Animation Fostivat" wibrent diveras animation and characters, oxtabrating the global art aniintatiinnn playfut dynamic design.
SOW
ANIMATION
ANIMATION
Ployground-42.55
503.5
Flac1.dev
Ideoggam-22
Geminz Dominz---aash--Ge
highlight misspelled or dis- torted text, while yellow boxes indicate redundant or missing text elements. Within the prompts, orange text denotes content and style requirements, and red text indicates textual elements. Com- pared to other methods, our approach produces cleaner layouts, better theme alignment, and more accurate text rendering under short prompts.
Figure 14: Visual comparison of different model outputs. Red boxes
BAGEL, Ideogram-v2). By contrast, PosterCraft generates outputs with coherent layouts, accu- rate text rendering, and strong adherence to detailed style requirements, achieving more natural visual-text alignment even under demanding long-prompt conditions.
setting of Fig 14 PosterCraft maintains strong balance between visual ap- peal and accurate text rendering- consistently integrates title and scene elements, for example, seamlessly embedding 'PixelPlay Retro Game Console" or 'International Animation Festival' in stylized compositions. Competing models often display legibility issues (e.g., SD3.5, Flux Idee), omit key poster elements (e.g.. BAGEL, Ideogram-v2), or suffer from text-scene disconnection (e.g.. Playground-v2.55 While Gemini2.0-Flash Gen performs well in text rendering, it still suffers from aesthetic limitations, often producing visually monotonous outputs with missing or underdeveloped design elements. Our results stand out with their vibrant layout, theme adherence, and natural visual-text coherence, even under minimal input conditions.
In the
We provide additional visual results generated by our PosterCraft framework to further demonstrate its capability in producing high-quality, aesthetically consistent posters. As shown in Fig 15 and [6][16] our model is able to seamlessly integrate text and imagery without requiring any external lay- out templates or modular refinement. The generated posters exhibit strong visual coherence text elements are not only stylistically aligned with the visual content but are sometimes cleverly em- bedded into the composition, enhancing the overall design fluency. From an aesthetic perspective, the model captures genre-specific styles across diverse themes, such as cinematic sci-fi, educational charts, cultural festivals, and commercial advertisements. It achieves a fine balance between visual richness and layout readability, effectively modeling principles such symmetry, emphasis, and hi- erarchy. These results underscore PosterCraft's 's potential as a powerful end-to-end tool for automatic poster generation with minimal input while maintaining high visual and semantic fidelity.
A.11 LIMITATIONS
In this work, we propose PosterCraft to explore how unified workflow design can unlock the aes- thetic design potential of powerful foundation models. Our results demonstrate that with carefully crafted design strategies, the model's capabilities can be significantly enhanced- makidng it compet- itive with leading proprietary commercial systems. This validates the soundness of our motivation. However, our approach is not without limitations. Specifically, our model is fundamentally built
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Origin Heteloom Reads
SURVIVAL FULL-TIRME
FANTAS MAP MAKING WORKSHOP
THE NOT BO- A0-SCARY MONSTER UNDER MY GED
WASTELAND DRIFTERS
AVALANCHE ANCHE PEAK
PAX
PATAGONIA EX SEEETIION
ROMANA
BILLY CRYSTAL 'Futtyy
CHRONDWREIE
ECHOES THE SHATISEED
Lenes Nen Yeur Lantern Festival
ROGUE PLANETX
TYDIe VISIONS
SUTVIALE ALL THAT REMAINS COMING SOON.
LRRDEERNNES
Figure 15: Examples generated by PosterCraft demonstrating high diversity and aesthetic quality across themes including education, entertainment, and science fiction. All generation results showcase genre-specific fidelity, text rendering, and layout aesthetic.
upon the current flux fluxl.dev baseline. As such, if the pre-trained flux model has never encountered certain types of samples or contains significant flaws, our method may not be able to fully correct these shortcomings. That said, our workflow is highly unified and readily transferable stronger baselines, ensuring full compatibility with other models in the community.
A.12 FUTURE WORK
In future work, we plan enhance our unified workflow in several directions. First, we will explore integrating PosterCraft with stronger backbone models and broader pretraining data, that our four- stage pipeline can further close the gap to the most advanced commercial systems while remaining architecture-agneee
Second, aim to scale up training with larger and more diverse datasets, and to adapt our tcxt-acsthetic preference design to product-poster scenarios where both product images and explicit layout hints provided, making PosterCraft complementary to frameworks such as PosterMaker. Third, we plan to extend our unified pipeline beyond aesthetic posters to other text-heavy domains (e.g., infographics and dense scene text) by redesigning the data and text- specific rewards while still avoiding heavy modular architectures. Finally, we will push towards multilingual (Chinese/English and more) poster generation, where cross-language typography and layout bring additional challenges but also offer an important real-world testbed for the scalability and cross-cultrall applicability our framework.
A.13 USAGE OF LLM
In preparing this manuscript, employed large language models exclusively for editorial purposes, such as polishing grammar, enhancing readability, and shortening overly complex sentences. At stage were ideas, methodologies, experiments, results, figures, references generated by
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URBAN CANVAC STREET ART EXPO
BLADE FINN RUNNER
KY DYSTOPA
THE NEBULA OSYSSEY SPACE
BRR KERR CTTADE ORROWES
RoboChef Snart Wichen foxbrarr
AuraGlow teee cyre
THE WARRIORS
MICRO-KNIGHT LENNA KAVIER
RYAM STONE
URBAN LEGENDS
Evergreen
ANIMATION
BLOOM
WHAT IF..?
MAIC
JOKER
Figure 16: Examples generated by our PosterCraft demonstrating high diversity and aesthetic quality across themes including movies, product, and virtual reality, All generation results showcase genre-specific fidelity, text rendering, and layout aesthetic.
LLM, nor unverifiable content introduced. All scientific contributions- iinluuding the design of the study, implementation of methods, execution of experiments, and validation of findings were entirely carried out and verified by the authors, who retain full responsibility for the final content.
Prompt A.1 (MLLM Scorer Prompt)
Does this poster contain large Billing Block Credit Block at the bottom "4K ultrahd"
text at the top?
Based on your judgment, use the closest option answer, and only return the label:
A. Yes. There is a large Billing Block or Credit Block at the bottom or "4K ultrahd" text at
the top.
B. No. There is no Billing Block or Credit Block at the bottom and no "4K ultrahd" text at
the top.
Figure 17: Prompt for MLLM Scorer HQ- C0Q-Postr--1O0
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Prompt A.2 (Gemini Caption Generation)
Please write a structured and detailed caption in a single paragraph for this poster, covering the following five aspects in order:
Poster Content-Deserrbe what is visually depicted.
Poster Styli-Describbe the visual artistic tone, such cinematic, surreal, minimalist, or other distinct aesthetics.
Poster Text Provide the exact words shown in the image (title, subtitle, slogan, etc.) and their overall communicative intent.
Text Style and Position- Describe the typography in detail, including font style, size, tex- ture, and how it visually blends contrasts with the background (e.g., carved into surface, embedded in light, wrapped by natural objects, etc.); also specify where each piece of text is positioned and its orientation angle in the frame.
Layout-Describes Describe how the all elements are arranged to guide the viewer's 's focus. Be specifie, descriptive, and cohesive. Keep the response between 200 and 300 words, written as a single paragraph. Avoid listing or enumeration. Do not mention any design tools generation methods. Write if for professional design catalog. highlighting how visual and typographic design choices form a unified and compelling narrative.
Figure 18: Prompt for Gemini Caption Generation in HQ-Poster-1000-
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Prompt A.3 (Gemini Mask Generation)
Detect all text regions in the image. For each text region, provide its bounding box in box 2d format [ymin, xmin, ymax, xmax]. The coordinates for each bounding box must be list of four integers [ymin, xmin, ymax, xmax], normalized to the range [O, 1000] J. Ensure the box completely covers the text area. MANDATORY GUIDELINES:
The box_2d coordinates [ymin, xmin, ymax, xmax] should be integers normalized to 0-1000.
If no text found the image, the "text.regions" list in the JSON output should be empty.
STRICT CONSTRAINTS:
Adhere strictly to the JSON output format specified below.
Do not include any explanations, apologies, or conversational text outside of the JSON structure.
Ensure the provided normalized coordinates are accurate.
RESPONSE FORMAT:
Respond with a single JSON object. Do NOT use markdown (c.g.. "Mnjson
")).
The JSON object must have single key "text regions".
The value of "text regi ons" must be a list of bounding boxes.
Each bounding must be a list of four integer coordinates [ymin, xmin, ymax, xmax], normalized to [0, 1000].
Example of the required JSON structure for "text.region""" regions" containing two bounding boxes:
[yminl, xmini, ymaxl, xmaxl [ymin2, xmin2, ymax2, xmax2
The complete JSON object should look like this:
"text_ "eett_regions":
// List of bounding boxes as shown above // e.g., [120, 10, 50, 1001, 170, 150, 100, 2501]
}
If no text is found, the output should be: ("text.regions": []]. Provide ONLY this JSON object.
Now, based SOLELY your comprehensive image analysis, provide ONLY the JSON object detailing all detected text regions and their normalized box_2d coordinates [ymin, xmin, ymax, xmax] specified.
Figure 19: Prompt for Gemini Mask Generation in Posseer-100K
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Prompt A.4 (Prompt Alignment Evaluation)
You expert in evaluating image content and font style against given text prompt. You will be given an image and an original text prompt that was intended to generate an image similar to the provided. Your task is assess whether the image is substantially consistent with the original text prompt based on the criteria below. Original Text Prompt: " "forigime ptt.pppttptttttttx.. Evaluation Criteria (Prioritized):
Text Accuracy:
Thoroughly analyze all text visible in the image. Check for any inaccuracies such typos, missing characters/words, pared to the "Original Text Prompt". This is the MOST CRITICAL factor. ANY such error is found, the decision MUST be "0".
extra characters/words when com-
2. Text Style and Positioning:
If text is present, does its style (font, color, decoration) and positioning (layout, orientation) the image reasonably align with what is described or implied in the "Original Text Prompt"?
3. Overall Content, Artistic Style, and Visual Appeal:
Does the overall image content (subjects, scene, objects) and artistic style align well with the "Original Text Prompt"?
Is the image generally clear, well-composed, and visually appealing in the context of the prompt?
Output Format: Based on your assessment, output ONLY a JSON object the following "1"}} } if the image is substantially consistent with the original prompt across the prioritized criteria (especially if no text errors are found when text is intended) and should be kept. {{" (nni deci is sion": discrepancies in Text Accuracy (typos, missing/extra characters/words), or significant issues in other critical criteria, or overall poor alignment, meaning the image should be discarded. Strict constraints:
format: ((ffnnalddecisi
"O"}} if there ANY
Only output the JSON object.
Do NOT include any additional text, explanation, or markdown.
Use exactly "0" "I" as the value for "fciio.decssoon".
Figure 20: Prompt for Prompt Alignment Evaluation in
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Prompt A.5 (Best-of-6 Selection)
You a professional Poster Designer. Your task evaluate six generated posters based on design brief ("Original Prompt") and select the single best poster, or indicate if none are suitable.
Evaluation Process:
1. Textual Accuracy (Paramount Importance):
First, assess all posters for textual accuracy against the "Original Prompt". Text (if any is specified or implied by the brief) MUST be perfectly accurate:
No typographical errors.
No missing or extra characters/words.
A poster with any textual flaw cannot be chosen as the best IF an alterna- tive poster with perfect text exists.
2. Content Alignment and Aesthetic Value:
This criterion is used to select among posters that have passed the textual accuracy check.
The chosen poster should:
Provide content as close as possible to the "Original Prompt".
Demonstrate the highest possible aesthetic value (considering composi- tion, color palette, typography, imagery, and overall visual impact).
Selection Logic:
Ideal Case: If more posters have perfect textual accuracy, select from THIS group the single poster that best meets Criterion 2 (Content Alignment and Aesthetic Value).
Special Case (All Posters Have Textual Flaws): If ALL six posters have some textual inaccuracies, then no poster meets the primary standard for "best." this situation, you MUST output "none".
Fallback Case (This should ideally not be reached if "Special Case" is handled correctly): the logic leads here unexpectedly after "Special Case" consideration, and no poster has perfect text, but selection is still forced, choose the poster that, despite its textual flaws, is superior when evaluated SOLELY on Criterion (Content Alignment and Aesthetic Value across all six flawed images). However, prioritize outputting "none" all have text flaws.
Original Prompt (Design Brief): " original_prompt""
Select the image ("1", "2", "3", "4", "5", "6". or "none") that best meets these requirements. Respond ONLY with ISON object in ONE of these exact formats: {{ ("besttimage"" "1"}} OR ( (("best_image
"none"]
Strict constraints:
Only output the JSON object.
Do NOT include any additional text markdown.
Use exactly "1", "2", "3", "4", "5", "6", or "none" to refer to your selection.
Figure 21: Prompt for Best-of-6 Selection in Rester-Reflect-- 120K.
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Prompt A.6 (Feedback Collection)
Internally compare the first poster against the second poster, focusing strictly visual con- tent layout and overall aesthetic style. Based on this internal comparison, provide detailed and specific suggestions in two aspects: 1. Poster Content Suggestions 2. Aesthetic Style Optimization Suggestions. Act as a professional poster designer. Deliver highly detailed, specific, and actionable feedback in the form of standardized image editing instructions. MANDATORY GUIDELINES:
The second poster must be fully followed as the standard. Identify and correct all visual layout and style discrepancies based this reference.
Focus exclusively on content and visual/aesthetic design. Completely ignore any issues related text, typography, wording, spelling, rendering, or text styling.
STRICT CONSTRAINTS:
NEVER mention the second poster, reference, target.
NEVER use comparative phrases such "similar to the second poster" or "make it like the second poster".
ONLY describe the editing instructions for Poster 1, framed as standalone improve-
ment tasks.
RESPONSE FORMAT: Response should be formatted as clearly structured json { ("Pster Content Suggestions" str, "Aesthetic style optimi zat ion siggestions". str} Return ONLY the ISON object itself, without any introductory text or markdown formatting.
schema:
Figure 22: Prompt for Feedback Collection in Dester-Reflect-1222
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Prompt A.7 (OCR Evaluation)
You OCR evaluation assistant. Follow these steps exactly the attached image:
1. Ground- Extraction (from the design prompt only):
Do NOT read text from the image for GT.
Parse ONLY the following design prompt and extract ALL text strings that should appear on the poster (titles, subtitles, dates, slogans, venue, etc.), pre- serving spaces and punctuation exactly: original prompt.toeteeeeee _ext""
Order them in spatial sequence (top->bottom, left->right) and concatenate into raw GT text.
2. OCR Extraction (from the attached image):
Run OCR on the provided image and extract ALL rendered text exactly as appears.
Preserve visual reading order (top-left--hottomm-ii))) This is your raw OCR text.
3. Text Normalization (apply to BOTH raw GT and raw OCR before comparison):
Convert all letters to lowercase.
Oi.... Collapse any sequence of whitespacccnewllnes into a single space.
Remove ALL punctuation characters:
Trim leading and trailing spaces.
4. Character-Level Alignment & Error Counting:
Align the normalized GT text and OCR text character by character.
Count four categories:
Insertion (I): extra character in OCR not in GT ("more").
Deletion (D): GT character missing in OCR ("less").
Substitution (S): OCR character differs from GT character ("render er-
ror").
Correct match (C): identical characters.
5. Metrics Calculation:
Let N total normalized GT characters = S.
Let total normalized OCR characters S
Let total compared characters S
Character Accuracy C/T.
Text
Text Recall = CC
Text F-score 2 & Precision Recall (Precision Recall).
6. Final ISON Output (strictly this format, no extra keys or commentary): "GT_text": "; "<normalized GT text." "totaL.TT.chars"" N, "correct.chars!! C, "insertions": I, "deletions":
{
"CCR_text",
"norraaiized OCR text>",
D,
"substitutions" S, "accuracy"; "XX.XX*" "recall": "23.
"precision": YYS"
score": "WW.Wи8"
Figure 23; Prompt for OCR Evaluation.
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Prompt A.8 (Preference Evaluation)
Your task is to evaluate single input image containing two sub-images side-by-side (Left: L, Right: R), both generated from the "Original Prompt". Compare them on Aesthetic Value, Prompt Alignment, Text Accuracy, and Overall Preference.
General Evaluation Protocol: For each of the four categories:
Provide a brief textual analysis justifying your choice.
2. Make definitive choice: "L" (Left is superior), "R" (Right is superior), or "none".
When to Choose "none": You must select "none" for category if:
a) I. and R are tied or indistinguishable in quality for that category.
b) The category is not applicable.
c) After careful review, you cannot definitively determine a superior side.
d) Crucially: and R exhibit clear, offsetting strengths and weaknesses within that specific category. If L excels in one aspect of the category while R excels in another, and these trade-off's make declaring an overall winner for that cat- egory difficult misleading, choose "none". Do not attempt to weigh these distinct, offssttiing pros and cons force a preference.
Your careful judgment vital.
Original Prompt: originall_pronee pprompt"
Please provide your evaluation in the JSON format specified below.
1. Aesthetic Value:
Evaluate visual appeal: harmony and consistency background style, text style (if present), thematic consistency between background/text, overall con- tent/text layout, and how the artistic style (background, content, text) aligns with the "Original Prompt".
aesthetiicvaalueespplattiio
Your brief analysis.
aesthetic..vauee: Choose "L", "R", or "none" (if L/R are equally pleasing/coherent,, choice is impossible, or they exhibit offsetting aesthetic strengths/weaknessees as per the "When to Choose 'none" " protocol).
Respond with:
"L/R/none",
"asschetic..aalue.ppplaaeee
"Your analysis... "}]
2. Prompt Alignment (excluding text elements and artistic style):
Evaluate how well non-textual elements (subjects, objects, scene) in I. and R match the "Original Prompt".
prompt ali allignment....m..mm expl anation": Your brief analysis. prompt..al a gngenttttt Choose "L.", "R", or "none" (if L/R align equally well/poorly, it's too close call, or they exhibit offsetting strengths in align- ment as per the "When to Choose "none"" protocol).
Respond with:
{{"prompt alignment": "L/R/none", proppt al alignment. ieplanatioon. "Your anal analysis....
Figure 24: Prompt for Preference Evaluation.
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Prompt A.9 (Preference Evaluation)
Text Accuracy (if applicable):
Evaluate text in I. and based only textual content specified/implied in the "Original Prompt".
Focus only on:
Accuracy: All prompt-specified words/characters present, no typos/mis- spellings/atteatioons?
Recall: All intended textual elements from prompt included? Any missing words/phrases?
Ignore text style, font, visual appeal, legibility (unless it prevents deter-
mining accuracy/recall), and placement.
ttext_accuuee aww.rracc...pppllaaatt.....caa.. explanation'. Your brief analysis.
deextaccuracy"
First, determine if "none" appropriate (as per the general "When to Choose 'none"" protocol, especially if L is better on Accuracy but R on Recall, or vice-versa; or if performance is identical/text N/A).
If "none" is not chosen, select "L" if is demonstrably superior overall in combined text accuracy and recall, or "R" if is.
Respond with:
(("textaacurracy"
"I/R/none", "Your analysis...
"texttaccuracy.xxxlaaattt.oo
2. Overall Preference:
Considering all above aspects (aesthetics, alignment, text accuracy) and any other factors relevant to the "Original Prompt".
Your brief analysis.
overall preference Choose "L", "R", "none" (if L/R are equally preferred, a choice impossible, or they present compelling but different and offsetting strengths across categories making neither holistically superior, as per the "When to Choose 'none" " protocol).
Respond with:
{["overall preference": "L/R/none",
"overall preference explanation": "Your analysis...")
Respond ONLY with single JSON object in the following format: "aesthetic.value"" syour_choice_for.aeseee aesthhtt beestheetic.aalue.eeplaaattt aesthetics..." "prompt.aliggmentt: "pzomptaallgmmnnt.xxxllaaaaatti
"Your brief analysis for
"Your brief analysis for
prompt alignment...""
"text accuracy": "your choice for text", "text_accoracyyeepllaaee
"Your brief analysis for text
accuracy...".
"overall.prefeeeenee"" "your.choice.forooeeralll
"Your brief analysis for
overall preference....
Replace placeholders with your choices ("L", "R", "none") and analyses.
Strict constraints:
Only output the JSON object.
No additional text or markdown.
Each choice value (e.g.. cresthetic_value"s must be "L", "R", or "none".
Explanation fields must contain your textual analysis.
Figure 25: Prompt for Preference Evaluation.
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Prompt A.10 (Final Retouching Instructions)
Character
You are a professional image retouching artist tasked with finalizing a single retouching approach based on the user's preferences and previous proposals. Your expertise ensures that the final approach integrates key aspects from different suggestions or follows a single selected approach in full.
Background
The user has reviewed previous retouching approaches and provided feedback or specific instructions for final retouching plan that aligns with their creative goals.
Ambition
Your goal is to either choose one of the previously proposed approaches that best matches the user's vision or create new, cohesive retouching approach by combining elements from different suggestions. Ensure that the final approach fully respects the user's instructions and creative intent.
User Instruction
User says: "[user_instruction)"
Task
Review the provided retouching approaches and the user's feedback or instructions.
2. Decide whether to:
Select single approach that fits the user's description.
Create new approach that integrates relevant aspects from different sugges- tions.
3. Final Approach:
Describe the adjustments to Light (exposure, contrast, highlights, shadows, blacks, and whites) and Color (temperature, tint, vibrance, and saturation). For each adjustment, specify which objects areas of the image are most affected and describe the specific details (e.g.. "the intricate carvings on the roof are highlighted by gentle increase in exposure").
Explain the expected visual effect on these objects, such as "the water reflec- tions appear richer and more defined" or "the sky becomes softer and more inviting."
For each individual HSL adjustment (Red, Orange, Yellow, Green, Cyan, Blue, Purple, Magenta), explain why is necessary and deseribe the expected visual change for specific objects (e.g., "the red tones in the window frames become more vivid emphasize their ornate design").
Organize the description as a step-by-step plan, indicating the sequence of adjustments.
Guidelines for Description:
Avoid providing exact numerical values focus on explaining how the adjustments affect the image's 's visual presentation.
Mention specific objects, areas, and their corresponding changes to help visualize the effect.
Ensure the approach remains detailed, logical, and cohesive, and does exceed 100 words.
Figure 26: Prompt for Final Retouching Instructions.
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Table 6: Effect of applying the full PosterCraft pipeline to SD3.5. Text F-score and HPSv3 are computed on our aesthetic text-to- poster prompts. The Gemini win rate vs. SD3.5 reflects overall preference between SD3.5 with our training pipeline and the vanilla SD3.5 baseline.
Model variant
Text F-score t HPSv3
Win rate vs. SD3.5 f
SD3.5 (baseline)
0.479
10.122
50.0
SD3.5 + full PosterCraft (4 stages)
0.554
10.330
58.1
A. 14
GENERALIZABILIT BEYOND FLUX-DEV: EXPERIMENTS ON SD3.5
To verify that our workflow is not tied to Flux-dev, we further apply the same four-stage post-training pipeline (text rendering - poster SFT with region-aware calibration + aesthetic-text preference RL + VLM reflection) to Stable Diffusion 3.5-Medium (SD3.5). We keep the SD3.5 architecture and sampler unchanged and only reuse our datasets and method designs.
Figure presents visual comparison on several poster prompts: the left column shows raw SD3.5 outputs, and the right column shows results after our PosterCraft pipeline. The optimized posters exhibit sharper and more consistent text, clearer layout structure, and improved global aesthetics, while preserving the semantics of the original prompts.
Quantitatively. Table reports text F-score on our text-to- rext-to-poster prompts, the HPSv3 aesthetic score, and the Gemini win rate when comparing SD3.5 with and without our post- tast-training. Our pipeline brings consistent gains on all three metrics, mirroring the trends observed on Flux-dev. This confirms that the proposed framework architecture-agnostic and can serve as general post-training recipe for modern diffusion backbones, rather than Flux-specific trick.
A.15 WHY UNIFIED POSTERCRAFT FRAMEWORK WORKS
Our goal is not to propose a new backhone, but to show that a single diffusion model can learn text, layout, and visual style for posters through carefully staged, unified workflow. Traditional poster systems typically decompose these factors into separate networks or ControlNet-style branches (c.g., independent text, layout, and style modules), which increases inference cost and makes global opti- mization difficult. PosterCraft instead keeps one backbone and upgrades it via four stages that are all defined on simple image-text text pairs.
This unified view is feasible because each stage is poster-specific yet complementary: (i) text- rendering stage first calibrates stroke-level accuracy and small-font legibility on synthetic Text- Render-2M without changing the architecture; (ii) a region-aware SFT stage on HQ-Poster- 100K rebalances losses over major text, minor text, and non-text regions so that the model learns realistic poster layouts and typography while preserving the strengthened text ability; (iii) aesthetic-text preference RL stage optimizes a single preference signal that jointly measures aesthetic quality and text correctness, instead of focusing only of them; and (iv) customized VLM-reflection stage feeds critique embeddings back into the prompt space, further refining layout and style with- adding heavy controllable signals liking general editing models.
Because all four stages share the same backbone and operate on unified supervision, PosterCraft learns trade off text, layout, and style within model rather than across loosely coupled mod- ules. Empirically, we observe consistent gains over the base FLUX and SD3.5 backbones on both poster benchmarks and generic text-rendering datasets, suggesting that the unified framework is not tied to particular architecture and can serve as general post- post-training recipe for high-quality, text-aware image generation.
A.16 DATASET AVAILABILITY AND STATISTICAL ANALYSIS
Public availability. As clarified in the main paper, all datasets in this work are automatically constructed from publicly available imagery rather than private or sensitive data. Text-Render 2M is built by synthetically rendering diverse texts onto high-quality real images drawn from publicly available image datasets (natural scenes, products, artworks, historical photos, etc.). HQ-Poster 100K is collected from publicly available poster resources with automatic filtering and text-mask
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generation. We will release: (i) the complete Text-Render-2M dataset (rendered image prompt), (ii) the full HQ-Pos Poster-100K poster images with prompts and text masks, and (iii) the PosterCraft model checkpoints trained on these data, under non-commercial research license. HQ-Poster- 100K contains third-party copyrighted materials and is released strictly for non-commercial research under fair-use principle; do not claim ownership of these materials, and downstream responsible for complying with applicable copyright regulations.
A.16.1 STATISTICS OF TEXT-RENDER -2
Text-Render-201 2M a synthetic data of rendered texts on real photographic backgrounds. We compute corpus-level and scene-level statistics over all rendered samples.
Table 7: Statistics of Text-Render-2M (rendered texts background images).
Aspect
Category
Portion
Text instances per image
instance
43%
2 instances
36%
3 instances
21 %
Text content type
Template-based grammatical phrases
71 %
Random alphanumeric strings
29%
Text length (words per instance)
1-5 words very short strings Short phrases (=:510 words)
23%
47%
Sentence-level text (#s 10-20 words)
Long sentences or short paragraphs (>~00 words)
8%
Orientation layout
Horizontal
61 %
Vertically rotated (90")
17%
Vertically stacked (onc character per line)
22%
Background seene type (underlying images)
Macro nature & ecology (insects, plants, fungi)
21 %
Everyday outdoors & landscapes
18%
Urban scenes & architecture People & portraits
16%
Products & still-life
14%
Artistic paintings & illustrations
10%
Historical / archival documentary photos
8%
These statistics show that Text- SokttRendee-2MM covers and substantial share of sentence- and paragraph-level text, while the photographic backgrounds span diverse natural, urban, artistic, and historical scenes rather than being limited to simple natural images.
layouts, several orientation modes,
A.16.2 STATISTIC' OF HQ-POSTER- 100K
Unlike the synthetic Text-Render-2M data, HQ-Poster-1000 consists of real-world aesthetic posters. Most posters revolve around main title, an optional tagline, and occasionally compact auxiliary information.
Taken together, these statistics highlight the complementary roles the two datasets in our training pipeline. Text--Rnder-22 provides controlled diversity in text length, orientation, and cross-domain photographic backgrounds, which crucial for robust text rendering. HQ-Poster-100K, in turn, sup- plies fully designed aesthetic posters where layout structure, visual style, and text-image integration are jointly optimized by human designers-exactee the style and layout distribution that the later stages of PosterCraft are trained model.
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Table Text statistics of HQ-Poster-100k 100K.
Aspect (Text)
Category
Portion
Notes
Text instances per poster
1-2 blocks
#48%
Mostly only the main title.
3 blocks
R336%
Title plus one or two short taglines or secondary blocks.
4 5 blocks
RJ 16%
Additional taglines or other information.
Text content type
Titles named entities
R:46%
Titles, character names, locations, etc.
Taglines / short phrases
R34
Promotional slogans, mood-sctting phrases.
Others
R/20%
Cast lists, logos, copyright lines, release information.
Text length (per block)
1-10 words (short)
772%
Short titles and concise taglines.
10 20 words (medium)
-21%
Longer slogans or bricf descriptions.
20 words (long)
R7%
Mainly dense billing blocks the bottom.
Table 9: Visual and aesthetic statistics of HQ-Poster- P00K
Aspect (Visual)
Category
Portion
Key descriptors
Visual style/ art direction
Cinematic / realistic
se63%
Dramatic lighting. clear depth of field, photographic look.
Illustrative graphic
drawn comic/graphic novel styles, painterly or pop
Minimalist symbolic
R=13%
Strong negative space. silhouettes, emblematic icons.
Subject matter/ / composition
Character portrait
R:43%
One or two close faces, expressive emotions.
Ensemble group composition
=266%
Multiple characters montage tiered layouts.
Action soene-driven
s:19%
Dynamic poses. chase scenes. large landscapes or cityscapes.
Object concept-centrie
R:12%
Key prop or symbolic object (helmett device, broken glass, etc.).
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This vertical poster "Sne Hutli Attonay of Law features spyized image of Charie Cor holatig
a
inpely wouning' ahe chest, siightly bubidng behind
light guy oingissses, looking coe contisent, lighthig with disilyyd prominacyy styloedi
exhouette
postiooed bottom right quadrant the poster, "Inc" amgleer potggrrotttttsttssooooooo above lagie, bosd
Discclly "Hull"" subotte SATTORNEY LAW, cheating stacked wousl hecorchy, Acroos top poster, in largo, MURDOCK:
silccpp,
purpli, capss sane-sent The composition emphasizes Most Mursbok the forear ound, centored withie the name. with the cityscage providiig o dynamio bochsonap, sutject hext the tep. overall layout craws the eye from the actorss a name chovacter nite arehe top. down portralt the the the bottom, creating a oleartiow
nestrontasty oneeted.
Stefffer HULiK
She HULK
the treamment placed bottornnghe nghh botance weightt
vlower.
WALLACGE KROOE CRODMAN
WALLLACE GROMAN
Triie pouttr feuturee advimsicat, attp-mottio aniimtted style, pactroping a glowts garder ghomm in whower gronn outhi, olue pants, and gray boots whit suft., nolding a longe par of open gardin shoa's above ni hoar againut maam, pale beige backggound The oust nncudas the nile "Wollace& & Gramit" in laght, orwag, Blocly, tiros-dimensiiaaa lattarig with subola caattar cutline and hightisht, aspearvog at the ice of posser and angled srightly dowmaard from loft to right. Bolow the tile, in smatler, jagges edged fore, the subrill VENGEANCE MOST rowE is ddpplayec io beight yeilow, pusiliured centrely and spacning the wcdth of the ittee, Above the main tete, the smolley black, sans send font the word "AARDMANN" is posttonedd signity the left, accompanied by swnall block star. The overalt poaditoo the cust promisaddy at the top, skowing the eye with ho bold colors and distinatio oppogrplyy, while the charming chavactor and aversiodd shaara are contoved below, proating bellnceed bettnoee/comppsiiee composition must is both playlu and wwg.ytyonnnnnn criiouus, hurning at the fim'eebieeddd of humoor and mystayy.
BRAIN
BRAN NUE DAE
st/ide This ribunt features blue with while confem. They
peopis, acuutts and
NIUE D AE
frrword, landscape joynse movement. the opboat celeeratscy, apthnlan
suggesing joumnyy collective blue and conveying of
The test 'BRAN NUE DAE", aupears story,
bis, blook englert sightly poster, compastion, with figure bollw providing buman inwhing chyy artventtrn.
upward from srighlyy appeaceros, binting at The expressions the towards Weely overall layout creates bolid actint www.er join
daminaes upper
amrngedd
The posterto for 'Sevaphim Falt" features BNo man, one in donk coat ant the other in grosn shift a viable hnletw and puttil atwnddig back-m--back in vast, and lanchccape under a diamaaiic aluudy sky, the scace evoless sence mh Westem genre and intense contict, conveying nagged and senoous tone through its onemmtic styn The prominent fext at the bottom of the posterreads "SERAPYIM FALLS, aevving an the nlints tiin The nite is presented a boit, didheossd, end sone-aent fore, appearing hochord and grety. as weathored by harsh endrronment aepicred. The toxt honzontally criented and contered at the bettom of the flame. The overall layout places the tiee figres prominently the mid ground, dominating the upper and central portions the poster, white the the anchors the compositio the base, drawing the veewe'' eye downwards after takingg in the characters and setting The stark contrest oetween the darkrgurss and ighter, dusty dantylaadsscapee combined with the intente cover and feotume of the toin creates sisuriy stiling andimpactio designn that strongfy communicatio the Ron'k Westemm there charrsettti renun..
SERAN IA PIHIM FEAAP FALS
SERAPHIM FALLS
The pester "Nomadiann"" features layered supormppsse "Nemasland, beady the groundted
protagonist the fonground, sentfort The and contemplative evoking sconery, and tnaataayy lnyout eye spwseds individual zasapping ands soaring eiyhhrhheeeehance the tim's treedoo trnsiencee within
viscal
anlety Sim's against poskiared honizontally the bettrn of the subtly prominaen minugg woman, creadig narrative connects the The qually
NIVALAND
NOMADLAND
Figure 27: Visual comparison of poster generation performance. The left column displays the raw outputs from the original SD3.5-Medium. The right column presents the results optimized via our PosterCraft pipeline. The text boxes on the right contain the complete input prompts used for generation. For fair comparison, the random seed is fixed at 0 for all inference steps.
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The whimaical poster for the fantady film Maonpatul Grave' depicts young ge with fuminous blue reaching out touch giant, glowing twnlght. forsar hiled oversized, fantastical falntly gloving
ilower enchanted with sthouetted, ancient trees in the doep blue background. The arhohic styte is richly illustrttve and magical, using paletto of deep blues, purples, and biclumineseent greens and yellows to create dreamille atmosphere. The fimm's tile, Moonpetal Grove, la rendered in an arnate, Rowing silver honzontally. The archad gracefully horizontal, layout guides viewr' eye from gold
sparkling frrotlis,
root capitai the enchanting thie down to the central interaction belveen the girt and the magical flowor. The glowing elemants and intricats datalls louite expioration world worder and enchaniment, perfectly uaplured the fantastical typogrohyy"
delicate, leaty tendrits Boneath the tagiine Where magic
promising
Moonpetac Grove
Moonpeall Gove Vlere nagictare
Moonpetal Grove,
Whe magic takeo mert
Whare magic
Postermaker
bizgen
ours
Figure 28: Qualitative comparison against layout based methods (Part 1). We present generation results from Postermaker (left), Bizgen (middle), and our PosterCraft (right). The corresponding creative briefs are displayed in the red boxes above each example. As illustrated, PosterCraft demon- strates superior performance in strict rendering, layout arrangement, and aesthetic alignment with the prompt, whereas baseline models frequently exhibit text hallucinations or fail to capture the specified atmosphere.
"Tiis desplate landssace. Jagged rock oranges,, reds, and shadlwy blacks create sense rugled isolation and impending conflict. film's tile, 'DUST DEVIL DUE, rendered in bold, heavily distressed, and spurred santl font, reminiscent of classic Wastemn typography. The letters are weathered, sandl yollow and appear to be brandled burnaa into dark, batt strip that runa horizontally acresa the bottom of poster. The title siihhly arched. The tagline, 'Venglance layout oplo
'Dust Devi's Due
freme the aithouetted
horsebook, riding
dust-choked
artisic style stark cinemetic,
drematic
desert diamatic dlverling lone rider, empheaizing solltaryjjounee The rugged, impactful thtl treatment the bottom grounds image and immedistely astabilshaa the finn's gavra and tone."
Dust Devl
Vengecnces Frides
Dust Devil,
Vangsonce pale
STRS WISTERN
DUST DEVE'S DUE PEKION,
Postermaker
bizgen
ours
Figure 29: Qualitative comparison against layout based methods (Part 2).
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Published as a conference paper at ICLR 2026
AA post poot-sppccalypiic survivs film poster features lone survivor, heavily closted, budijing through anoe-coveedd, ruined city, toyel dog by their side, The sty is perpetual, tosic yeiloer-gnyy The style deselats and griny amphaabzing hardshhi and matienne, with mused, cold cator palateo. The title, blocky, ice-blue sans font
WINTER," rendered buried the snow. actor's name, I'AMA TAY2 OR-JOX subily placed small, white, weathered at top. layout corveys serse vast empétnass and the archous jounny the protagonist, with the typagrephy reflacting the harsh envianment."
postioned horizontatly very bottom,
ARNTTYLO
THE LONG WNTER 1333"
ANYA TYYLOR
ANYA
LONG WINTER
AA
Postermaker Figure 30: Qualitative comparison against layout based methods (Part 3).
bizgen
ours
sovanture poster flest Viking longships saiing through stormy, The style dramatic and painturly, with sonan of nagedd power and imponaing conftict, using a palatto of dork bluss, groys, end flashes of lightning. The title (WORTHWIND SAGA, IsIs randered in neary, runic unic-inspirad, metallic silver font with weathered testure, positioned horizontally the smaller, classic
towerts rugged, mist-shrouded conatline.
bottom,
ancient. nito.. The ayaggaptyyrrnngtionn strangthens
\'ALEXANDER SKAPSGARD! and 'WICOLEE KIDMAN," mythlogicca and historical themea."
EIDID ADKD NORTHWIND SAGA
EEII
SAGEA
NORTHWIND SAGA
Postermaker
bizgen
ours
Figure 31: Qualitative comparison against layout based methods (Part 4).
40

---

Published as a conference paper at ICLR 2026
drame poster features sihouwete couple embracing sgainst wibrent, abstract watercoler beckground swiling pinks, purples, thte, I'COLORS OF THE HEART! wrltien in an alagant, flawiing white sclint font, posiiianed dieginally across the center of the watercoior swirt, hianding with the artwork. The tagline, VLove paints its own picture, i in amallor, delicata serif font below. The typography soft and romantic, pertactly complementing
blues, representing their umotional world. style artiatic and emonive, focussig on feeling of love rather than realistic depiction.
visuals."
COLORS OF THEHE Love paints Irert
Colors the Teart
Zon pood Ammtich
Postermaker
bizgen
ours
Figure 32: Qualitative comparison against layout based methods (Part 5).
"This advertieement for 'Dasa Infusions' botanical features sleek, clear glass bottle gin, with delicate illustaaiooss juniper barriss, citrus peal, orystal
visible labal, sophisticated, refreshing, artisanal, emphasizing natural ingredients promium quality: contemporary sertf font subtte siver, pesitioed horiznntally at the top. The tagline Sip Serenity: is in smaller, elagant script font white, contored below the bottle and glass. Grofhed with Wild Sotanicals's aisereatly printed the bottle label. The loyour is eingant and visually appeating. neture
fresh batanioels
beckground soml refined
The
Desi infusions,
cocktail
the sophiatcated typography
brand's
idendit."
OASIS
Oasis Inusois A Sip of Serenit
Oasis Infusions, A sip of Serenity.
ASepp Serranty
BOANTD
Postermaker
bizgen
ours
Figure 33: Qualitative comparison against layout based methods (Part 6)
41

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Published as a conference paper at ICLR 2026
depicts charming gourcs, set against backdrap of golden autumn trees and a distent farmhouse. Warm, dappled sunlight filters through the leoves. The style warm, invting. and traditonal, celebrating the bounty sutumn. The festival mis, Ember Grove, is in friendly, hend-lettered, slihhly whiresical sent font rich burgundy, arcing ahove the 'Fall Hervest Festival's is in smaller, rustic sans-aeri in dark orangs, beneath the nile, Live Music Arissan Grafts) sens-sert
overllowing with pumpkins, apples,
Fresh Produce
Willow conterplecs, and the charning typograpyy enhancing festivl's warm, community-tooue focused atmosphore.""
picturasque
Ember & Grove Fall Harvest Festival
Embeer Grove Farve
The Ember & Gove
&GGVE The Ember & Grove,
FEOVEIW
Lte Music Artiss Craffs Freck Triskey
Out 15 16 | Willow Creek Firm
Postermaker
bizgen
ours
Figure 34: Qualitative comparison against layout based methods (Part 7).
"This for Crimson Tide Surf Competiton's showcases drematic, action-packed photograph of surfer massive, curling silhoustted against fiery
mid-air. style exhilaratin,, powerful, adventurous, capturing raw energy of surfing. The compettion tite, CRIMMSN TIDE, is in botd, dynemmic, sans-cert font with slight wave-like distortion, rendered in stark white that outs through the vibrant sunset uolors, posiiioeed borizontally acroes the top third of the poster. Wenual Surf Compettion is in smeller, sharp sans- serf in yallow, beneath the fitl.. Pipeline Beach October 5-7 Prizes & Glory! is the bottorn clean white sans-sent The lsyout impactful, anargatic typography
orange
soray
visual canveying intenslty sport,
thilinng
compehtive nature."
CRIMSON TIDE
Crinson Tide Sur
Crimson Tide Surf Competition,
October Pelif 5-7
Postermaker
bizgen
ours
Figure 35: Qualitative comparison against layout based methods (Part 8).
42

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Published as a conference paper at ICLR 2026
'A poster for The Albi/ Speakeday Lounge' features a dimnyy it, vintage Interio shot: a polished mahogany bar, amber glow from behind bottles, classic cooktail in crystal glass, and hint of oigar smoke. The sore clandestine, sophisticaed,, and nostalgic, evoking the Prohibition era. The lounge nome, THE ALiBI, frosted glees panel. Speakeesy Lounge'
subtle shadow, postiooned borizontally the Whispers Whistey Password Required Find the Red Door on Eim Street the bottom in discreet, white, typewitee-s//e font. The layout is stmosphenic and intriguing, with the dimiy iit bar setting mysterious tone, and the vintage upography perfectly captuning the speakeasy's exclusive and secretive charm."
elegant, art-deco inspired serif font, muted
appearing
THE ALIBI Spenkeary Leunge
Alibi speakeasy lounge, eakeasy Lounge
Ropuire
The Albi Speauk
PLEAFASSY IE OLE
Postermaker
bizgen
ours
Figure 36: Qualitative comparison against layout based methods (Part 9).
43


Reviews:
[{"review_id": "ELXKI247YY", "reviewer": "Reviewer_xLFo", "summary": "PosterCraft is a framework for high-quality, aesthetic poster generation that moves beyond traditional modular pipelines and rigid layouts. The approach integrates four cascaded stages: large-scale text rendering optimization using the new Text-Render-2M dataset, region-aware supervised fine-tuning (HQ-Poster-100K), aesthetic-text reinforcement learning via best-of-n preference optimization, and joint vision–language feedback refinement. Each stage is supported by automated, stage-specific dataset construction, enabling robust training without complex architectural changes. PosterCraft outperforms some recent methods in text rendering, layout coherence, and visual appeal, as demonstrated by both quantitative metrics and user studies.", "questions": "Please refer to the detailed questions raised in Weakness section above.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "+ The proposed abandons modular and layout-constrained designs, enabling holistic integration of text, layout, and artistic content for visually coherent posters.\n\n+ The work also introduces and leverages large, high-quality, and stage-specific datasets, supporting robust and scalable training.\n\n+ The proposed method outperforms some recent methods in text accuracy, aesthetics, and prompt alignment.", "weaknesses": "- When talking about text rendering performance, it is usually important to measure text redner quality and accuracy under different length of words - from simple to complex, e.g. <20 words, 20-60 words, > 60 words. The current work lacks such kind of measurements making it hard to justfiy its strength especially for compelx cases. \n\n- Text rendering is an important area includes multiple areas including poster, infographic and scene text. It is not clear why the work only limited to the poster domain while not show generalization on others text rendering domains. \n\n- Authors claim state-of-the-art performance for the proposed method. However, many important methods are missing for the evaluation for both open-sourced and commercial ones, which makes it hard to support authors' claim or strength of the proposed method. \nOpen-sourced:\nGlyph-ByT5 [a] and BizGen [b], PosterMaker [c], as well as \n\n[a] Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering, ECCV 2024\n[b] BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation, CVPR 2025\n[c] PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering, CVPR 2025\n\n Commercial ones:\n[A] recraft, [B] GPT-4o", "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": "EwfovViyhf", "reviewer": "Reviewer_X3rD", "summary": "The paper PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework presents an end-to-end system for generating visually compelling and textually accurate posters through a unified cascaded workflow. Instead of relying on modular pipelines or predefined layouts, PosterCraft integrates four stages—scalable text rendering optimization, high-quality poster fine-tuning, aesthetic–text reinforcement learning, and joint vision–language feedback refinement—each supported by automatically constructed datasets. This framework enables the model to synthesize posters with coherent layouts, stylistic harmony, and precise typography directly from textual prompts. Experiments demonstrate that PosterCraft substantially surpasses existing open-source baselines in both rendering fidelity and overall aesthetics, achieving performance close to leading commercial systems such as Gemini2.0-Flash-Gen.", "questions": "1. Have the authors validated this workflow's generalizability by applying it to other foundational models (e.g., Stable Diffusion 3.5 or others)? Without such experiments, it is difficult to determine if the proposed pipeline is a truly generalizable framework or a series of optimizations highly specific to the Flux architecture.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces four large-scale, automated datasets (Text-Render-2M, HQ-Poster-100K, etc.) tailored for specific training stages. This pipeline provides high-quality, specialized data for text rendering, style fine-tuning, and preference learning, addressing a major bottleneck in the field.\n\n2. The paper proposes a unified framework that abandons rigid, modular pipelines where layout and text are generated separately. This approach allows the model to holistically explore coherent combinations of text, art, and layout in one process, resulting in more aesthetically harmonious compositions.", "weaknesses": "1. The claimed “unified generative framework” mainly integrates existing methods rather than introducing a fundamentally new generative modeling concept. Each stage—text rendering optimization, preference learning with DPO, and vision-language feedback—relies heavily on prior work. As a result, the contribution is more engineering-oriented than algorithmically innovative, making the paper better suited to an application or dataset construction area rather than the core generative models track.\n\n2. Each stage requires a specialized dataset and specific training configurations (e.g., full-parameter finetuning, LoRA, DPO). This makes the end-to-end training pipeline exceedingly cumbersome and expensive. Consequently, the high complexity and cost create significant barriers to reproduction and practical deployment.\n\n3. The authors claim the method is universal, but all experiments were conducted on a single base model, Flux-dev. Without experiments on other foundational models, it is impossible to know if this is a truly generalizable framework or just a deep over-fitting customized specifically for Flux-dev.", "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": "6b1QV7bYN3", "reviewer": "Reviewer_wnAm", "summary": "This work proposes a full pipeline and corresponding datasets for achieving high-performing poster generation.", "questions": "I notice that in Tab.3 and Tab.4, PosterCraft, while demonstrating very competitive text rendering capability, still lags behind the topmost models (TextCrafter and X-Omni). I am fine with this gap, as text rendering is not the most important component of this work. But I am curious about how the authors would attribute this gap to. For example, is the gap due to base model limitation, data quantity/quality, or the absence of specially-designed algorithms? And as an extension, in the authors' opinion, what are the most important and promising directions to further enhance the model's text rendering capability?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "In general, the paper looks good to me.\n\n1. It shows a good example of the full building pipeline of a high-quality domain-specific image generation system. Core procedures like data collection/curation, preference alignment, and reflection optimization are not only covered but accomplished at high quality.\n\n2. The proposed datasets are very helpful to this field.", "weaknesses": "None", "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": "2HKlu0o2eQ", "reviewer": "Reviewer_rUYo", "summary": "This work proposes an unified diffusion-based framework for high-quality aesthetic poster generation. Authors design an end-to-end optimization workflow to jointly learn text rendering, artistic layout, and stylistic coherence.", "questions": "Generating highly realistic text in an end-to-end manner remains a significant challenge, as it often leads to structural distortions in character shapes. The results presented by the authors are surprisingly impressive in this regard. The authors’ results are impressive—how was this achieved in an end-to-end setup?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "This work achieve visually coherent results by integrating text, layout, and artistic style in a single generative process.\nAuthors proposes a high-quality Text-Render-2M dataset.\nPosterCraft outperform all open-source baselines (Flux1.dev, SD3.5, BAGEL, etc.) and nearly match the closed-source Gemini-2.0-Flash-Gen.", "weaknesses": "The motivation for adopting a unified framework appears questionable. Visual styles and textual elements often have fundamentally different requirements, visual styles typically emphasize global consistency, whereas text demands attention to fine-grained details and stroke-level accuracy.How does the proposed method reconcile these competing objectives?\n\nThe paper lacks sufficient comparison with recent academic works, such as Postermaker[1]. It is recommended that the authors provide a more detailed discussion of how their approach differs from or improves upon these recent methods to better highlight the novelty and contribution of this work.\n[1] Postermaker: Towards high-quality product poster generation with accurate text rendering\n\nWhile the dataset scale is impressive, the lack of public availability and insufficient statistical analysis limit its value to the research community.", "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)