EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection
Authors: Wenxin Tang, Jingyu Xiao, Yanpei Gong, Fengyuan Ran, Tongchuan Xia, Junliang Liu, Man Ho Lam, Wenxuan Wang, Michael R. Lyu
Organizations: Tsinghua University, Beijing, China · The Chinese University of Hong Kong, Hong Kong, China · Harbin Institute of Technology, Harbin, China · Wuhan University, Wuhan, China · Beijing University of Posts and Telecommunications, Beijing, China · Dalian Maritime University, Dalian, China · Renmin University of China, Beijing, China
Automated academic poster generation aims to distill lengthy research papers into concise, visually coherent presentations. Existing Multimodal Large Language Models (MLLMs) based approaches, however, suffer from three critical limitations: low information density in full-paper inputs, excessive token consumption, and unreliable layout verification. We present EfficientPosterGen, an end-to-end framework that addresses these challenges through semantic-aware retrieval and token-efficient multimodal generation. EfficientPosterGen introduces three core innovations: (1) Semantic-aware Key Information Retrieval (SKIR), which constructs a semantic contribution graph to model inter-segment relationships and selectively preserves important content; (2) Visual-based Context Compression (VCC), which renders selected text segments into images to shift textual information into the visual modality, significantly reducing token usage while generating poster-ready bullet points; and (3) Agentless Layout Violation Detection (ALVD), a deterministic color-gradient-based algorithm that reliably detects content overflow and spatial sparsity without auxiliary MLLMs. Extensive experiments demonstrate that EfficientPosterGen achieves substantial improvements in token efficiency and layout reliability while maintaining high poster quality, offering a scalable solution for automated academic poster generation. Our code is available at https://github.com/vinsontang1/EfficientPosterGen-Code.
Figures & tables
Figure 1. An overview of the workflow of existing automated poster generation approaches, along with the major challenges they encounter in practice.
Figure 2. The framework of EfficientPosterGen. The framework of \framework.
Figure 3. An example of the layout verification process. (a) Input panel image. (b) Gradient magnitudes of vertical strips (bottom curve) and horizontal strips (left curve), with activated strips highlighted in blue (vertical) and yellow (horizontal). (c) Cartesian product of activated strips yields content regions ( green ) and their minimum enclosing rectangle ( red ). (d) Overflow detection via the red bounding box exceeding panel boundaries, and sparsity detection via the green coverage ratio. A four-panel figure illustrating the \Mthreealgorithm. Panel (a) shows an input poster panel containing text and a figure. Panel (b) displays gradient magnitude curves along the bottom edge for vertical strips and along the left edge for horizontal strips, with activated vertical strips in blue and activated horizontal strips in yellow. Panel (c) shows the Cartesian product of activated strips as green regions and their minimum enclosing rectangle in red. Panel (d) demonstrates the overflow detection result, with the red bounding box and green content regions used for layout verification.
Efficiency
Vis. Quality & Txt.coherence
VLM-as-Judge
PaperQuiz
Model
Token(K) ↓
Cost()\downarrow$
Vis.Sim.(%) ↑
PPL ↓
Fig.Rel.(%) ↑
Aesthetic ↑
Information ↑
Verbatim ↑
Interpretive ↑
Oracle Methods
Paper
-
-
53.00
4.60
22.00
3.58
4.22
87.72
86.92
GT Poster
-
-
-
11.26
21.00
3.56
3.98
116.02
142.09
End-to-end Methods
5-HTML
26.90
0.1097
66.75
10.24
22.14
3.49
3.85
109.94
130.90
Table 1. Overall comparison of poster generation methods across efficiency, visual quality, and downstream evaluation metrics. Bold indicates the best result and underline indicates the second-best result for each metric. API pricing details used for cost estimation are provided in Table 16 .
Figure 4. Parameter sensitivity analysis. (a) Entropy reduction ratio under varying β and γ . (b) Layout detection accuracy across strip numbers N and activation thresholds τs . (c)-(d) Compression ratio vs. normalized edit distance at varying DPI for GPT-5 and Qwen3-VL-8B-Instruct.
Figure 5. Examples of posters generated by different methods.
Efficiency
Setting
Token(K) ↓
Cost()\downarrow$
VLM-as-Judge ↑
PaperQuiz ↑
EfficientPosterGen
10.35
0.0016
3.64
133.35
w/o SKIR
12.10
0.0018
3.53
129.33
w/o VCC
14.48
0.0020
3.59
131.32
w/o ALVD
84.78
0.0076
3.27
125.82
Table 2. Overall performance of ablation study.
Appendix figures & tables20 assets
Supplementary material from the paper’s appendix.
Appendix
Vis. Quality & Txt.coherence
Aesthetic ↑
Information ↑
Model
Vis.Sim.(%) ↑
PPL ↓
Fig.Rel.(%) ↑
Element
Layout
Engage.
Avg
Clarity
Content
Logic
Avg
Overall ↑
OWL-4o
54.00
11.46
-
2.76
3.62
2.56
2.98
3.92
2.89
3.36
3.39
3.19
PPTAgent-4o
50.00
6.20
16.00
2.49
3.05
2.45
2.66
2.05
1.26
1.38
1.56
2.11
Ours-4o
75.23
8.96
23.87
3.92
3.71
2.93
3.52
4.09
3.41
3.66
3.72
3.62
Appendix
Table 3. Comparison of poster generation methods on visual quality, text coherence, and VLM-as-Judge evaluation using GPT-4o as the backbone. Bold denotes the best result in each column.
Raw Accuracy ↑
Density-Augmented Score ↑
Verbatim
Interpretive
Model
open-source
closed-source
V-Avg
open-source
closed-source
I-Avg
Overall
V-Avg
I-Avg
Overall
OWL-4o
47.87
31.96
39.92
49.94
74.38
62.16
51.04
78.69
122.91
100.80
PPTAgent-4o
39.63
11.99
25.81
36.22
37.15
36.68
31.25
51.62
73.37
62.49
Ours-4o
57.40
50.63
54.02
53.56
79.50
66.53
60.27
107.99
132.96
120.47
Appendix
Table 4. Detailed PaperQuiz evaluation results using GPT-4o as the backbone. Raw Accuracy and Density-Augmented Score are reported for both verbatim and interpretive questions, further broken down by open-source and closed-source reader models. Bold denotes the best result in each column.
Figure 6. Human preference evaluation results.
Method
Token ↓
Time (ms) ↓
Accuracy (%) ↑
Overflow F1 (%) ↑
Valid F1 (%) ↑
Sparse F1 (%) ↑
Qwen3-VL-8B-Instruct
2672.5
7256.5
62.0
66.7
36.4
72.7
GPT-5
2265.2
9148.1
72.7
86.7
44.1
75.6
EasyOCR
0
6139.9
71.3
99.0
29.5
70.0
Our Method
0
186.5
94.0
100.0
90.3
91.6
Appendix
Table 5. Comparative results of layout detection methods on the ternary classification dataset. Our method achieves the highest accuracy while requiring no token overhead and maintaining significantly lower latency.
Aesthetic
Information
Model
Element ↑
Layout ↑
Engage. ↑
Overall ↑
Clarity ↑
Content ↑
Logic ↑
Overall ↑
Oracle Methods
Paper
4 .05
3 .89
2.80
3.58
4.00
4.68
3.98
4.22
GT Poster
4.07
3.90
2.70
3.56
4.09
3 .96
3.89
3 .98
End-to-end Methods
5-HTML
4.02
3.64
2 .81
3.49
4 .11
3.62
3.81
3.85
Appendix
Table 6. Fine-grained VLM-as-Judge evaluation across aesthetic and informational dimensions. Aesthetic quality is decomposed into element design, layout composition, and visual engagement, while informational quality is assessed along clarity, content coverage, and logical coherence. Bold and underline denote the best and second-best results, respectively.
Raw Accuracy ↑
Density-Augmented Score ↑
Verbatim
Interpretive
Verbatim
Interpretive
Model
4o-mini
o3
Overall
4o-mini
o3
Overall
4o-mini
o3
Overall
4o-mini
o3
Overall
Oracle Methods
Paper
67.26
94.13
80.70
65.74
94.28
80.01
73.13
102.31
87.72
71.41
102.42
86.92
GT Poster
55.49
67.10
61.30
64.82
85.54
75.18
105.08
126.95
116.02
122.77
161.40
142.09
End-to-end Methods
Appendix
Table 7. Detailed Aug-PaperQuiz evaluation results. Raw Accuracy measures the proportion of correctly answered questions by each reader model. The Density-Augmented Score further adjusts Raw Accuracy with a length-based penalty that rewards concise posters, defined as sa=sr(1+max(1,L/W)1) , where L is the poster text length and W is the median length of human-designed posters. Results are reported separately for two reader models (GPT-4o-mini and o3) along with their overall average. Bold and underline denote the best and second-best results, respectively.
Setting
Value
page-size
A4(595,842)
margin-x
10
margin-y
10
font type
Verdana
font size
10
line height
10
Appendix
Table 8. Fundamental typesetting configurations.
Model
DPI
CR
dedit
GPT-5
24
3.09
0.922
48
3.09
0.782
96
1.40
0.074
144
0.81
0.003
196
0.62
0.002
Qwen3-VL-8B-Instruct
24
8.03
0.753
Appendix
Table 9. OCR-based evaluation results across different DPI settings.
N
τs
Accuracy
256
0.3
0.767
256
0.5
0.780
256
0.7
0.653
512
0.3
0.920
512
0.5
0.940
512
0.7
0.780
Appendix
Table 10. Classification accuracy for layout overflow detection.
Efficiency
Vis. Quality & Txt.coherence
VLM-as-Judge
PaperQuiz
Setting
Token(K) ↓
Cost()\downarrow$
Vis.Sim.(%) ↑
PPL ↓
Fig.Rel.(%) ↑
Aesthetic ↑
Information ↑
Verbatim ↑
Interpretive ↑
EfficientPosterGen
10.23
0.0016
77.32
8.76
25.43
3.53
3.76
119.53
147.17
w/o SKIR
12.10
0.0018
75.23
8.83
23.98
3.42
3.64
117.13
141.54
w/o VCC
14.46
0.0020
74.21
9.12
24.27
3.50
3.68
118.31
144.33
w/o ALVD
84.91
0.0076
75.11
9.07
24.19
2.90
3.65
111.22
140.43
Appendix
Table 11. Ablation study on the main evaluation metrics. Each row removes one component from the full EfficientPosterGen pipeline. Bold denotes the best result in each column.
Aesthetic
Information
Setting
Element ↑
Layout ↑
Engage. ↑
Overall ↑
Clarity ↑
Content ↑
Logic ↑
Overall ↑
EfficientPosterGen
3.94
3.71
2.93
3.53
4.11
3.47
3.69
3.76
w/o SKIR
3.82
3.61
2.83
3.42
4.07
3.31
3.55
3.64
w/o VCC
3.92
3.68
2.91
3.50
4.07
3.43
3.54
3.68
w/o ALVD
3.47
2.91
2.33
2.90
3.91
3.46
3.69
3.65
Appendix
Table 12. Detailed VLM-as-Judge ablation results. Aesthetic quality is decomposed into element design, layout composition, and visual engagement, while informational quality is assessed along clarity, content coverage, and logical coherence. Bold denotes the best result in each column.
Raw Accuracy ↑
Density-Augmented Score ↑
Verbatim
Interpretive
Verbatim
Interpretive
Setting
4o-mini
o3
Overall
4o-mini
o3
Overall
4o-mini
o3
Overall
4o-mini
o3
Overall
EfficientPosterGen
54.24
65.28
59.76
63.37
83.80
73.59
108.48
130.57
119.53
126.74
167.59
147.17
w/o SKIR
54.05
64.53
59.29
62.05
81.08
71.57
106.73
127.52
117.13
122.86
160.22
141.54
w/o VCC
53.67
64.88
59.28
62.75
82.93
72.84
107.11
129.50
118.31
123.19
165.47
144.33
w/o ALVD
52.80
58.66
55.73
61.61
79.14
70.38
105.35
117.08
111.22
122.93
157.93
140.43
Appendix
Table 13. Detailed PaperQuiz ablation results. Raw Accuracy measures the proportion of correctly answered questions, while the Density-Augmented Score adjusts Raw Accuracy with a length-based penalty that rewards concise posters. Results are reported separately for two reader models (GPT-4o-mini and o3) along with their overall average. Bold denotes the best result in each column.
Textual Token(K)
Visual Token(K)
Summary Token(K)
Model
Input ↓
Output ↓
Input ↓
Output ↓
Total Input ↓
Total Output ↓
Total ↓
End-to-end Methods
5-HTML
18.21
8.69
0.00
0.00
18.21
8.69
26.90
PosterAgent Variants
PosterAgent-5
31.49
10.30
187.67
24.91
219.16
35.21
254.37
PosterAgent-Qwen
36.80
6.00
82.16
0.29
118.96
6.29
125.25
Appendix
Table 14. Detailed token consumption analysis of each method. Bold denotes the best result in each column.
Textual Token(K)
Visual Token(K)
Summary Token(K)
Setting
Input ↓
Output ↓
Input ↓
Output ↓
Total Input ↓
Total Output ↓
Total ↓
EfficientPosterGen
5.94
1.91
2.56
0.00
8.50
1.91
10.41
w/o SKIR
6.05
1.98
4.08
0.00
10.12
1.98
12.10
w/o VCC
12.48
1.97
0.00
0.00
12.48
1.97
14.44
w/o ALVD
11.76
1.92
71.22
0.00
82.98
1.92
84.90
Appendix
Table 15. Detailed token consumption analysis of each ablation setting. Bold denotes the best result in each column.
Model
Input ($/M tokens)
Output ($/M tokens)
GPT-5-20250807
1.25
10.00
Qwen3-VL-8B-Instruct
0.08
0.50
Appendix
Table 16. API pricing per million tokens (in USD) as of February 6, 2026.
Figure 7. Case study on the paper InfinityGAN: Towards Infinite-Pixel Image Synthesis . Gray regions indicate areas outside the poster canvas boundaries.
Figure 8. Case study on the paper An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network . Gray regions indicate areas outside the poster canvas boundaries.
Figure 9. Case study on the paper VideoComposer: Compositional Video Synthesis with Motion Controllability . Gray regions indicate areas outside the poster canvas boundaries.
Figure 10. Case study on the paper Towards Reasoning-Aware Explainable VQA . Gray regions indicate areas outside the poster canvas boundaries.
Figure 11. Case study on the paper Uni MASK Unified Inference in Sequential Decision Problems . Gray regions indicate areas outside the poster canvas boundaries.
Scientific poster construction compresses a long multimodal paper into a readable, editable canvas. Existing systems hide request-level failures by scoring only completed outputs; direct image generation is not element-editable, while coding-agent workflows are costly. PosterMELD is a template-conditioned multi-agent pipeline: capacity-aware slots guide writing before rendering, and deterministic gates plus vision-language model (VLM) review route failures to bounded repair. Each accepted request exports editable PowerPoint (PPTX) and Portable Network Graphics (PNG) artifacts; explicit design controls yield same-paper variants. Across 621 papers, Print-Ready Rate (PRR) counts requests passing geometric, readability, asset-integrity, and obvious-factual-error checks, with native editability reported separately. A frozen VLM assigns conditional Craftsmanship-Harmony-Expressiveness (CHE) scores to print-ready outputs. PosterMELD attains 81.3% PRR, 3.4 times P2P's rate and 5.2 times PosterGen's, and the highest conditional CHE among generated methods with multiple print-ready outputs. Native editability and explicit design controls are retained at a mean cost of USD 0.38 per request, 3.5% of Codex+Skill's. Code and resources are available at https://github.com/Shannon4Science/PosterMELD.
Haojie Hu, Chenhao Dang, Yaojia Liu +3
Tsinghua Shenzhen International Graduate School, Tsinghua University · Tongji University · Shanghai Jiao Tong University +2
Visual posters are a compact medium for communicating dense information, yet progress on automatic poster generation remains difficult to measure because existing evaluations are often restricted to paper-only inputs, narrow domains, or surface-level visual similarity. We introduce Any2Poster Bench, a benchmark for any-source poster generation that evaluates systems across eight input modalities--PDFs, URLs, PPTX, DOCX, Markdown, LaTeX, notebooks, and videos--and five content domains. Any2Poster Bench pairs each source with quiz-based probes of verbatim factual retention and interpretive understanding, together with VLM-based judgments of visual quality, layout, readability, content completeness, and logical flow, enabling reproducible assessment of both information fidelity and visual communication. To instantiate and validate this benchmark, we further present Any2Poster Agent, an end-to-end reference agent that parses heterogeneous sources, organizes salient content, plans poster layouts, renders posters, and iteratively refines them using visual feedback. On Any2Poster Bench, Any2Poster Agent achieves 87.25% average accuracy across input modalities and 87.28% across content domains. On PaperQuiz-style evaluation, where prior paper-to-poster agents are directly comparable, Any2Poster Agent improves over PosterAgent-4o from 51.06-51.33% to 72.58% overall accuracy and from 116-121 to 145.16 in density-augmented score. Together, Any2Poster Bench and Any2Poster Agent provide a reusable evaluation resource and a competitive baseline for studying multimodal, domain-general poster generation.
Amogh Vinaykumar, Aiden Li, Suozhi Huang +1
Flower Mound High School · University College London · Princeton University
We present iPoster, an interactive layout generation framework that empowers users to guide content-aware poster layout design by specifying flexible constraints. iPoster enables users to specify partial intentions within the intention module, such as element categories, sizes, positions, or coarse initial drafts. Then, the generation module instantly generates refined, context-sensitive layouts that faithfully respect these constraints. iPoster employs a unified graph-enhanced diffusion architecture that supports various design tasks under user-specified constraints. These constraints are enforced through masking strategies that precisely preserve user input at every denoising step. A cross content-aware attention module aligns generated elements with salient regions of the canvas, ensuring visual coherence. Extensive experiments show that iPoster not only achieves state-of-the-art layout quality, but offers a responsive and controllable framework for poster layout design with constraints.