PaintBench

Deterministic Evaluation of Precise Visual Editing

Anonymized release for double-blind review.

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A precise, deterministic visual editing benchmark. Evaluates whether native pixel-space image generation models can execute "MS-Paint-style" edits — geometric transforms, color changes, structural manipulation, and symbolic-reasoning edits — with pixel-level correctness. Every problem is a (input_image, instruction, answer_image) triplet generated programmatically, so the answer is pixel-exact and the answer distribution is known by construction.

This dataset ships two configurations that share the same schema and evaluation pipeline:

Config	Splits	Problems	Scope
PaintBench	test, dev	2,016 + 280	Main benchmark — 20 tasks × 8 visual conditions × 12 problems-per-cell
TinyGrafixBench	test	600	Chart-edit micro-benchmark — 5 chart types × 4 subtasks × 30 problems

Quick start

from datasets import load_dataset

# Full PaintBench test set (1,920 scored problems + 96 preservation diagnostic)
ds = load_dataset("PaintBenchICLR2027/PaintBench", "PaintBench", split="test")
print(ds[0]["instruction"])
ds[0]["input_image"].show()
ds[0]["answer_image"].show()

# Stratified 280-problem dev split for fast iteration
dev = load_dataset("PaintBenchICLR2027/PaintBench", "PaintBench", split="dev")

# Filter by visual condition (one of 8 perturbation axes)
n_xhigh = ds.filter(lambda r: r["visual_condition"] == "n_xhigh")

# Filter to scored problems only (exclude the preservation diagnostic)
scored = ds.filter(lambda r: r["task"] != "preservation")

# Chart-edit subset
charts = load_dataset("PaintBenchICLR2027/PaintBench", "TinyGrafixBench", split="test")

Dataset structure

Schema (both configs)

Column	Type	Description
category	string	Top-level grouping. PaintBench: one of 4 task categories; TGF: one of 5 chart types.
task	string	Canonical task name (e.g. translation, bar_chart_add_bar).
mode	string	Subtask / variant within a task, or "default" for tasks with no mode dimension (7 single-mode PaintBench tasks + all TGF tasks).
visual_condition	string	One of 8 PaintBench-side perturbation axes, or "" for TGF rows (no visual-condition axis).
problem_id	int32	Index within the (category, task, mode) cell.
instruction	string	Natural-language editing instruction.
input_image	image	The source image to edit.
answer_image	image	The pixel-exact expected output.
metadata	string	JSON dump of the per-problem context (seed, scene shapes / colors, condition parameters, etc.).

PaintBench task categories

Category	Tasks
geometric_transformation	translation, rotation, reflection, scaling, shearing
structural_manipulation	construction, removal, copying, border, cropping
color_change	recolor, flood_fill, blending, gradient, point_operations
symbolic_reasoning	comparison, ordering, pattern, counting, legend

The preservation task (96 problems, exposed as task=="preservation") is a diagnostic that copies the input as the expected output and is excluded from aggregate scoring.

Visual conditions

Each PaintBench problem is rendered under one of 8 conditions; each varies exactly one axis from baseline:

visual_condition	What varies	Detail
baseline	(nothing)	1024 × 1024 canvas, default palette, default density
horizontal	canvas aspect ratio	1024 × 576
vertical	canvas aspect ratio	576 × 1024
nonstandard	palette	Non-standard color palette
striped	background	Striped (vs. solid) background
n_med	scene density	Medium-density scene
n_high	scene density	High-density scene
n_xhigh	scene density	Extreme-density scene

Each (visual_condition, task, mode) cell contains 12 / num_modes problems (12 for single-mode tasks, 6 each for two-mode, 4 each for three-mode). Across all 20 scored tasks this is 1,920 problems (240 per visual condition); the dev split picks the slot=0 problem per cell for a 280-problem stratified subsample.

TinyGrafixBench subtasks

Chart type	Subtasks
bar_chart	add_bar, sort_bars, remove_bar, recolor_bar
heatmap	add_cell, shift_heatmap, mask_cells, change_colormap
line_chart	draw_segments, normalize_series, filter_series, shade_interval
network	add_node, swap_nodes, remove_node, recolor_node
scatter_plot	draw_best_fit_line, swap_axes, remove_outlier, recolor_class

The dev split

PaintBench config ships a 280-problem dev split alongside the full 2,016-problem test split. Construction: one problem per (visual_condition, task, mode) cell across the 20 scored tasks (35 task-modes × 8 visual conditions = 280; preservation excluded). The dev split is a strict subset of the test split, generated deterministically (slot=0 of each cell), and is intended for fast model iteration — not as a standalone benchmark. Reproducibility: the same row's (task, mode, visual_condition, problem_id) quadruple uniquely identifies it in both splits.

Evaluation

Per-problem scoring is pixel-comparison-based: for each pixel, compute the CIE76 ΔE between the model's output and the expected answer_image, threshold at multiple ΔE levels (0, 1, ..., 10), and compute IoU and edit / preservation accuracy. Aggregate scores use macro-averaging at the task / category / visual_condition / benchmark levels with task-bootstrap 95% CIs to match the sampling unit of the displayed mean.

License

Released under Creative Commons Attribution 4.0 (CC BY 4.0). All problems and images are generated programmatically — no third-party content.