Repository profile
shitagaki-lab/see-through
"Single-image Layer Decomposition for Anime Characters" (SIGGRAPH 2026, Conditionally Accepted)
Why this page exists
Use this profile to move from awareness into adoption-oriented inspection.
Best next step
Check the summary, then compare it against similar projects before touching production.
Research posture
Momentum helps discovery. Fit, maintenance quality, and reversibility decide adoption.
Editorial summary
See-through is an innovative framework designed for the automatic transformation of static anime illustrations into manipulatable 2.5D models. By decomposing a single image into semantically distinct layers, the framework enables users to create detailed representations of characters with up to 23 individual layers, including features such as hair, face, eyes, clothing, and accessories. This technology, presented at SIGGRAPH 2026, greatly enhances the creative potential for artists and developers working with anime-style graphics, allowing for more dynamic and interactive applications of their art.
Adoption analysis
Best-fit use case
shitagaki-lab/see-through is most useful to evaluate when your team is researching Jupyter Notebook ecosystem tooling. Compare its documented workflow with your runtime, deployment model, and maintenance capacity before adopting it.
Momentum signal
Recent tracked star growth is modest, so maintenance quality and fit may matter more than momentum. Daily and three-day changes are discovery signals, while total stars show accumulated awareness.
Adoption caution
Before adding it to production, review license terms, dependency footprint, security guidance, open issue quality, and whether there is a clear path to migrate away later.
What to inspect next
- 1Run the quick install in a disposable project before touching production code.
- 2Check whether the README clearly states the project scope and non-goals.
- 3Identify at least two alternatives so the decision is not based on one ranking page.
- 4Read recent issues and releases to understand maintenance rhythm, breaking changes, and common failure modes.