SEAM-Bench

A double-blind continuity storyboarding benchmark for industrial short-drama generation.

SEAM-Bench standardizes the evaluation of visual continuity in AI short-drama storyboarding and supports reproducible assessment at both the prompt layer (storyboard text) and the image layer (rendered keyframes). It is the benchmark released alongside SEAM (Shot Entity-Attribute Memory), a training-free, model-agnostic memory graph that repairs cross-shot and cross-episode continuity at the storyboard prompt-text layer, deployed as a stage of the SEAM-Agent multi-agent storyboarding pipeline.

SEAM-Bench 是面向工业级短剧生成的双盲连续性分镜基准，用于在提示词层 （分镜文本）与图像层（渲染关键帧）上可复现地评测视觉连续性。

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Contents

The benchmark covers three produced short dramas, 68 episodes in total.

Drama (dir)	Title	Episodes	Human storyboards	Reference images
beyond-the-wall	Beyond the Wall	20	659	31 (26 char + 5 scene)
his-toyboy	His Toyboy: The Billionaire's Trap	23	754	15 (8 char + 7 scene)
werewolf	You Are My Cure, My Undoing	25	826	12 (12 char)
Total		68	2,239	58

Human-storyboard counts follow the aligned, annotated shot totals reported in the paper; raw CSV row counts run higher because a single shot cell may span multiple text lines.

Two kinds of material are released:

1. Original scripts — per-episode screenplays, a tiered outline, and character bios. These form the pipeline input.
2. Reference images — character and scene visual anchors, semantically renamed and deduplicated into a shared pool. They serve as the fixed keyframe input and as the image-layer consistency gold standard. For distribution the images are resized to a 1536px long edge (PNG containers preserved); this is ample for their use as reference anchors.

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Directory layout

dramas/
  <drama>/
    script/                     # pipeline input (screenplays + metadata)
      index.json                # title, logline, per-episode scene/character index
      tiered_outline.txt        # multi-level story outline
      character_bios.txt        # character descriptions
      camera_grammar.json       # per-character shot-grammar rules (his-toyboy only)
      episodes/
        epNN_cn.txt             # Chinese screenplay for episode NN
        epNN_en.txt             # English screenplay (his-toyboy only)
    director/                   # human-director storyboards, one CSV per episode
      epNN.csv
    character/                  # character reference images
      _pool/                    # deduplicated unique images
      reference_index.json      # role/state -> image mapping, per episode
    scene/                      # scene reference images (absent for werewolf)
      _pool/
      scene_index.json

script/index.json

{
  "title": "His toyboy: The Billionaire's Trap",
  "logline": "...",
  "num_episodes": 23,
  "volumes": ["第一卷：...", "..."],
  "episodes": {
    "1": {
      "title": "The Mark (咬痕)",
      "summary": "...",
      "scenes_en": ["EXT. MEDICAL UNIVERSITY COMMENCEMENT - STADIUM - DAY", "..."],
      "characters_en": ["JULIAN", "VICTOR", "..."],
      "scenes_cn": ["..."], "characters_cn": ["..."],
      "num_lines_en": 72, "num_lines_cn": 69
    }
  }
}

director/epNN.csv

Human-director storyboards. UTF-8 with BOM. Columns (Chinese headers):

Column	中文	Meaning
分镜号 / 集序号	shot no.	Shot index within the episode
场景	scene	Scene / location label
画面内容	visual	Visual description of the shot
景别	shot size	e.g. 全景 / 中景 / 特写
拍摄角度	angle	e.g. 平视 / 俯拍 / 仰拍
运镜	movement	Camera movement
角色	characters	Characters active in the shot
台词	dialogue	On-screen dialogue
中文台词	dialogue (zh)	Chinese dialogue (where separated)

Reading the CSVs: cells may contain embedded newlines and commas, so parse with a proper CSV reader (e.g. Python csv, pandas.read_csv) rather than splitting on commas. Files are UTF-8 with a BOM — read with encoding="utf-8-sig".

character/reference_index.json and scene/scene_index.json

Map each per-episode reference to a deduplicated image in _pool/:

{
  "drama": "his-toyboy",
  "pool_dir": "_pool",
  "unique_images": 8,
  "episodes": {
    "ep01": [
      {
        "role": "朱利安",
        "state": "julian_毕业典礼",
        "image": "_pool/julian_毕业典礼.png",
        "description": "朱利安，26岁，惊人漂亮但脸色苍白，毕业典礼上的毕业袍形象"
      }
    ]
  }
}

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Quick start

import csv, json
from pathlib import Path

drama = Path("dramas/his-toyboy")

# 1. Story metadata
index = json.loads((drama / "script/index.json").read_text(encoding="utf-8"))
print(index["title"], index["num_episodes"])

# 2. A human-director storyboard (BOM-safe, multiline-cell-safe)
with open(drama / "director/ep01.csv", encoding="utf-8-sig", newline="") as f:
    shots = list(csv.DictReader(f))
print(len(shots), "shots in episode 1")

# 3. Resolve a character reference image
refs = json.loads((drama / "character/reference_index.json").read_text(encoding="utf-8"))
for r in refs["episodes"]["ep01"]:
    print(r["role"], "->", drama / r["image"])

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Notes and known gaps

Coverage is not uniform across the three dramas; the benchmark reflects the material as produced.

• English screenplays (episodes/*_en.txt) exist only for his-toyboy; beyond-the-wall and werewolf ship the Chinese screenplays only.
• Scene reference pool is absent for werewolf (no scene/ directory).
• camera_grammar.json (per-character shot-grammar rules) is provided only for his-toyboy.
• werewolf/director/ additionally contains an aggregated ... - 人类分镜总表.csv (all-episode master table) alongside the per-episode CSVs.
• Reference images (character/_pool/, scene/_pool/) are stored via Git LFS on the Hugging Face Hub.

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Download

The benchmark is hosted as a Hugging Face dataset. Clone with git (LFS-backed) or pull via the huggingface_hub client:

# Option A: git clone (install git-lfs first)
git lfs install
git clone https://huggingface.co/datasets/Jackyqq/SEAM-Bench

# Option B: Python client
pip install huggingface_hub
python -c "from huggingface_hub import snapshot_download; \
snapshot_download('Jackyqq/SEAM-Bench', repo_type='dataset', local_dir='SEAM-Bench')"

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Copyright and intended use

The screenplays, human-director storyboards, and reference images in this benchmark originate from produced commercial short dramas and are released for non-commercial research on storyboard continuity evaluation only. They remain the property of their respective rights holders. Do not redistribute for commercial purposes or use them to train generative models for commercial release. If you are a rights holder and have concerns about any material here, please open an issue.

本基准中的剧本、人类导演分镜表与参考图源自已制作的商业短剧，仅供分镜连续性 评测的非商业研究使用，版权归各自权利人所有。

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Citation

If you use SEAM-Bench, please cite the accompanying paper:

@inproceedings{seam2027,
  title     = {SEAM: Shot Entity-Attribute Memory for Consistent Short-Drama Storyboarding},
  author    = {},
  booktitle = {Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
  year      = {2027}
}