See Through Anime Layer Decomposition

This breaks down a single anime illustration into up to 23 separate, fully inpainted layers with depth ordering, then exports everything as a PSD ready for 2.5D animation workflows. Built on SDXL and a fine-tuned Marigold depth model, it handles semantic splits like hair, face, eyes, clothing, and accessories while completing occluded regions behind each part. You'd use this when you need manipulatable layers from flat art without manually masking everything. The heuristic post-processing scripts let you further stratify layers by depth or left-right position. Installation is heavy with optional tiers for different annotators, and you need to run everything from repo root. The result is production-ready layered files, not just segmentation masks.

skills.sh installs: 706
GitHub stars: 61
last updated: Jul 14, 2026

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Install

$ npx -y skills add aradotso/trending-skills --skill see-through-anime-layer-decomposition --agent claude-code

Installs into .claude/skills of the current project.

Files

See-through: Anime Character Layer Decomposition

Skill by ara.so — Daily 2026 Skills collection.

See-through is a research framework (SIGGRAPH 2026, conditionally accepted) that decomposes a single anime illustration into up to 23 fully inpainted, semantically distinct layers with inferred drawing orders — exporting a layered PSD file suitable for 2.5D animation workflows.

What It Does

Decomposes a single anime image into semantic layers (hair, face, eyes, clothing, accessories, etc.)
Inpaints occluded regions so each layer is complete
Infers pseudo-depth ordering using a fine-tuned Marigold model
Exports layered .psd files with depth maps and segmentation masks
Supports depth-based and left-right stratification for further refinement

Installation

# 1. Create and activate environment
conda create -n see_through python=3.12 -y
conda activate see_through

# 2. Install PyTorch with CUDA 12.8
pip install torch==2.8.0+cu128 torchvision==0.23.0+cu128 torchaudio==2.8.0+cu128 \
  --index-url https://download.pytorch.org/whl/cu128

# 3. Install core dependencies
pip install -r requirements.txt

SKILL.md on GitHub

Details

Repository: github.com/aradotso/trending-skills
First seen: May 16, 2026