How does ViStoryBench define and score character consistency for story visualisation (metrics, dataset, headline numbers, validity checks, stated limits), and which components are under which licence?
Corrected 2026-10-23 - see C3-B681-01; the original statements below are kept for history and are superseded where the correction says so. ViStoryBench (arXiv 2505.24862v5, 'Accepted by CVPR 2026' per the arXiv API comment) provides 80 stories with 344 characters, 509 reference images, 1,317 shots across 10 style genres (39 photorealistic), in English and Chinese, and 12 automated metrics: cross/self style similarity (CSD), cross/self character identification similarity (CIDS: Grounding DINO crops, ArcFace/AdaFace/FaceNet for realistic characters and CLIP otherwise, bipartite matching), VLM-judged prompt alignment (scene, shot, character interaction, individual action), on-stage character count, aesthetics, Inception score and a copy-paste detector. Table 2 gives the copy-paste baseline (CIDS cross 0.929) as a reference and e.g. OmniGen2 0.548, UNO 0.485 and StoryDiffusion 0.397/0.436 on cross-CIDS. Self-CIDS correlates with a user study at Kendall 0.4978, Spearman 0.6759, Pearson 0...
Structured fields
{
"citations": [
{
"url": "https://arxiv.org/html/2505.24862v5",
"note": "candidate evidence excerpts"
},
{
"url": "https://github.com/ViStoryBench/vistorybench/tree/b44ec9108668cc2bcc8c5280886b235e9fb8bea9",
"note": "candidate evidence excerpts"
},
{
"url": "https://huggingface.co/datasets/ViStoryBench/ViStoryBench/tree/92f845531b67e97a67ae04b256ec5d8c020e8341",
"note": "candidate evidence excerpts"
},
{
"url": "https://export.arxiv.org/api/query?id_list=2505.24862,2405.01434,2402.03286,2311.10093,2312.04461,2401.07519&max_results=10",
"note": "candidate evidence excerpts"
},
{
"url": "https://raw.githubusercontent.com/ViStoryBench/vistorybench/b44ec9108668cc2bcc8c5280886b235e9fb8bea9/README.md",
"note": "correction source (record C3-B681-01): README note after the evaluation CLI options; same note"
}
]
}Evidence claims
- [sourced_fact] ViStoryBench (arXiv 2505.24862v5, 'Accepted by CVPR 2026' per the arXiv API comment) provides 80 stories with 344 characters, 509 reference images, 1,317 shots across 10 style genres (39 photorealistic), in English and Chinese, and 12 automated metrics: cross/self style similarity (CSD), cross/self character identification similarity (CIDS: Grounding DINO crops, ArcFace/AdaFace/FaceNet for realistic characters and CLIP otherwise, bipartite matching), VLM-judged prompt alignment (scene, shot, character interaction, individual action), on-stage character count, aesthetics, Inception score and a copy-paste detector. Table 2 gives the copy-paste baseline (CIDS cross 0.929) as a reference and e.g. OmniGen2 0.548, UNO 0.485 and StoryDiffusion 0.397/0.436 on cross-CIDS. Self-CIDS correlates with a user study at Kendall 0.4978, Spearman 0.6759, Pearson 0.7956 (excluding outliers). Authors state no background-reference evaluation, VLM hallucination risk, uncontrolled language effects and a fair-use claim for third-party images. Components are licensed separately: paper text CC BY-NC-ND 4.0, evaluation code Apache-2.0, dataset MIT (README/card), evaluator weights third-party. · locator: candidate evidence excerpts source
Rights
| component | license | hosting | redistribution | modification | training | Evidence and notes |
|---|---|---|---|---|---|---|
| documentation | unknown | permitted | permitted | unknown | unknown | Applies to this original summary only, not to the cited sources. |
Linked records
- cites → https://arxiv.org/html/2505.24862v5
- cites → https://export.arxiv.org/api/query?id_list=2505.24862%2C2405.01434%2C2402.03286%2C2311.10093%2C2312.04461%2C2401.07519&max_results=10
- cites → https://github.com/ViStoryBench/vistorybench/tree/b44ec9108668cc2bcc8c5280886b235e9fb8bea9
- cites → https://huggingface.co/datasets/ViStoryBench/ViStoryBench/tree/92f845531b67e97a67ae04b256ec5d8c020e8341
- cites → https://raw.githubusercontent.com/ViStoryBench/vistorybench/b44ec9108668cc2bcc8c5280886b235e9fb8bea9/README.md