Grounded Generation of Embellished Bar Chart Ensuring Chart Integrity
Bibliographic record
Abstract
Grounded image generation enables precise spatial control over pre-trained diffusion models, making it possible to use chart images as visual guides during the image generation process. This paper presents a novel approach that generates cohesive and natural illustrations of vertical bar charts by integrating real-world object images as visual embellishments. The proposed pipeline takes an object image and a reference bar chart as input and produces an embellished bar chart that follows the structure of the input chart. To preserve chart integrity by maintaining the count, position, size, and order of data values, we introduce a strategy that anchors the top and bottom parts of the object image to the top and bottom of each bar while allowing the middle section to be filled by the generation model. We demonstrate the efficacy of the pipeline through the generation of 4,725 chart images followed by evaluation based on three integrity metrics. The results show that generation success rate is affected by various factors. Finally, we discuss future directions for generalization and better usability of our pipeline, and limitations of evaluation used in our approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".