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---
language:
- en
pretty_name: 'Comics: Pick-A-Panel'
tags:
- comics
dataset_info:
- config_name: char_coherence
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 379249617.0
num_examples: 143
download_size: 379268925
dataset_size: 379249617.0
- config_name: sequence_filling
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 1230082746.0
num_examples: 262
download_size: 1153097954
dataset_size: 1230082746.0
- config_name: text_closure
features:
- name: sample_id
dtype: string
- name: context
sequence: image
- name: options
sequence: image
- name: index
dtype: int32
- name: solution_index
dtype: int32
- name: split
dtype: string
- name: task_type
dtype: string
- name: previous_panel_caption
dtype: string
splits:
- name: val
num_bytes: 952974973.0
num_examples: 274
download_size: 930660064
dataset_size: 952974973.0
configs:
- config_name: char_coherence
data_files:
- split: val
path: char_coherence/val-*
- config_name: sequence_filling
data_files:
- split: val
path: sequence_filling/val-*
- config_name: text_closure
data_files:
- split: val
path: text_closure/val-*
---
# Comics: Pick-A-Panel
This is the dataset for the [ICDAR 2025 Competition on Comics Understanding in the Era of Foundational Models](https://rrc.cvc.uab.es/?ch=31&com=introduction)
The dataset contains five subtask or skills:
### Sequence Filling
<details>
<summary>Task Description</summary>
Given a sequence of comic panels, a missing panel, and a set of option panels, the task is to select the panel that best fits the sequence.
</details>
### Character Coherence, Visual Closure, Text Closure
<details>
<summary>Task Description</summary>
These skills require understanding the context sequence to then pick the best panel to continue the story, focusing on the characters, the visual elements, and the text:
- Character Coherence: Given a sequence of comic panels, pick the panel from the two options that best continues the story in a coherent with the characters. Both options are the same panel, but the text in the speech bubbles is has been swapped.
- Visual Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the visual elements.
- Text Closure: Given a sequence of comic panels, pick the panel from the options that best continues the story in a coherent way with the text. All options are the same panel, but with text in the speech retrieved from different panels.
</details>
### Caption Relevance
<details>
<summary>Task Description</summary>
Given a caption from the previous panel, select the panel that best continues the story.
</details>
## Loading the Data
```python
from datasets import load_dataset
skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "val", "test"
dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
```
<details>
<summary>Map to single images</summary>
If your model can only process single images, you can render each sample as a single image:
```python
from PIL import Image, ImageDraw, ImageFont
import numpy as np
from datasets import Features, Value, Image as ImageFeature
class SingleImagePickAPanel:
def __init__(self, max_size=500, margin=10, label_space=20, font_path="Arial.ttf"):
self.max_size = max_size
self.margin = margin
self.label_space = label_space
# Add separate font sizes
self.label_font_size = 20
self.number_font_size = 24
self.font_path = font_path
def resize_image(self, img):
"""Resize image keeping aspect ratio if longest edge > max_size"""
if max(img.size) > self.max_size:
ratio = self.max_size / max(img.size)
new_size = tuple(int(dim * ratio) for dim in img.size)
return img.resize(new_size, Image.Resampling.LANCZOS)
return img
def create_mask_panel(self, width, height):
"""Create a question mark panel"""
mask_panel = Image.new("RGB", (width, height), (200, 200, 200))
draw = ImageDraw.Draw(mask_panel)
font_size = int(height * 0.8)
try:
font = ImageFont.truetype(self.font_path, font_size)
except:
raise ValueError("Font file not found")
text = "?"
bbox = draw.textbbox((0, 0), text, font=font)
text_x = (width - (bbox[2] - bbox[0])) // 2
text_y = (height - (bbox[3] - bbox[1])) // 2
draw.text((text_x, text_y), text, fill="black", font=font)
return mask_panel
def draw_number_on_panel(self, panel, number, font):
"""Draw number on the bottom of the panel with background"""
draw = ImageDraw.Draw(panel)
# Get text size
bbox = draw.textbbox((0, 0), str(number), font=font)
text_width = bbox[2] - bbox[0]
text_height = bbox[3] - bbox[1]
# Calculate position (bottom-right corner)
padding = 2
text_x = panel.size[0] - text_width - padding
text_y = panel.size[1] - text_height - padding
# Draw semi-transparent background
bg_rect = [(text_x - padding, text_y - padding),
(text_x + text_width + padding, text_y + text_height + padding)]
draw.rectangle(bg_rect, fill=(255, 255, 255, 180))
# Draw text
draw.text((text_x, text_y), str(number), fill="black", font=font)
return panel
def map_to_single_image(self, examples):
"""Process a batch of examples from a HuggingFace dataset"""
single_images = []
for i in range(len(examples['sample_id'])):
# Get context and options for current example
context = examples['context'][i] if len(examples['context'][i]) > 0 else []
options = examples['options'][i]
# Resize all images
context = [self.resize_image(img) for img in context]
options = [self.resize_image(img) for img in options]
# Calculate common panel size (use median size to avoid outliers)
all_panels = context + options
if len(all_panels) > 0:
widths = [img.size[0] for img in all_panels]
heights = [img.size[1] for img in all_panels]
panel_width = int(np.median(widths))
panel_height = int(np.median(heights))
# Resize all panels to common size
context = [img.resize((panel_width, panel_height)) for img in context]
options = [img.resize((panel_width, panel_height)) for img in options]
# Create mask panel for sequence filling tasks if needed
if 'index' in examples and len(context) > 0:
mask_idx = examples['index'][i]
mask_panel = self.create_mask_panel(panel_width, panel_height)
context.insert(mask_idx, mask_panel)
# Calculate canvas dimensions based on whether we have context
if len(context) > 0:
context_row_width = panel_width * len(context) + self.margin * (len(context) - 1)
options_row_width = panel_width * len(options) + self.margin * (len(options) - 1)
canvas_width = max(context_row_width, options_row_width)
canvas_height = (panel_height * 2 +
self.label_space * 2)
else:
# Only options row for caption_relevance
canvas_width = panel_width * len(options) + self.margin * (len(options) - 1)
canvas_height = (panel_height +
self.label_space)
# Create canvas
final_image = Image.new("RGB", (canvas_width, canvas_height), "white")
draw = ImageDraw.Draw(final_image)
try:
label_font = ImageFont.truetype(self.font_path, self.label_font_size)
number_font = ImageFont.truetype(self.font_path, self.number_font_size)
except:
raise ValueError("Font file not found")
current_y = 0
# Add context section if it exists
if len(context) > 0:
# Draw "Context" label
bbox = draw.textbbox((0, 0), "Context", font=label_font)
text_x = (canvas_width - (bbox[2] - bbox[0])) // 2
draw.text((text_x, current_y), "Context", fill="black", font=label_font)
current_y += self.label_space
# Paste context panels
x_offset = (canvas_width - (panel_width * len(context) +
self.margin * (len(context) - 1))) // 2
for panel in context:
final_image.paste(panel, (x_offset, current_y))
x_offset += panel_width + self.margin
current_y += panel_height
# Add "Options" label
bbox = draw.textbbox((0, 0), "Options", font=label_font)
text_x = (canvas_width - (bbox[2] - bbox[0])) // 2
draw.text((text_x, current_y), "Options", fill="black", font=label_font)
current_y += self.label_space
# Paste options with numbers on panels
x_offset = (canvas_width - (panel_width * len(options) +
self.margin * (len(options) - 1))) // 2
for idx, panel in enumerate(options):
# Create a copy of the panel to draw on
panel_with_number = panel.copy()
if panel_with_number.mode != 'RGBA':
panel_with_number = panel_with_number.convert('RGBA')
# Draw number on panel
panel_with_number = self.draw_number_on_panel(
panel_with_number,
idx,
number_font
)
# Paste the panel with number
final_image.paste(panel_with_number, (x_offset, current_y), panel_with_number)
x_offset += panel_width + self.margin
# Convert final_image to PIL Image format (instead of numpy array)
single_images.append(final_image)
# Prepare batch output
examples['single_image'] = single_images
return examples
from datasets import load_dataset
skill = "sequence_filling" # "sequence_filling", "char_coherence", "visual_closure", "text_closure", "caption_relevance"
split = "val" # "val", "test"
dataset = load_dataset("VLR-CVC/ComPAP", skill, split=split)
processor = SingleImagePickAPanel()
dataset = dataset.map(
processor.map_to_single_image,
batched=True,
batch_size=32,
remove_columns=['context', 'options']
)
dataset.save_to_disk(f"ComPAP_{skill}_{split}_single_images")
```
</details>
## Summit Results and Leaderboard
The competition is hosted in the [Robust Reading Competition website](https://rrc.cvc.uab.es/?ch=31&com=introduction) and the leaderboard is available [here](https://rrc.cvc.uab.es/?ch=31&com=evaluation).
## Citation
_coming soon_ |