# LayerBuilder: Layer Decomposition for Interactive Image and Video Color Editing

arXiv: https://arxiv.org/abs/1701.03754 · HTML: https://mlsanity.com/paper/1701.03754/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2017.

## Built on by (most cited first, 7 of 7)

- [Text2LIVE: Text-Driven Layered Image and Video Editing](https://mlsanity.com/paper/2204.02491/) (2022)
- [Layered Neural Atlases for Consistent Video Editing](https://mlsanity.com/paper/2109.11418/) (2021)
- [Shape-aware Text-driven Layered Video Editing](https://mlsanity.com/paper/2301.13173/) (2023)
- [PaletteNeRF: Palette-based Color Editing for NeRFs](https://mlsanity.com/paper/2212.12871/) (2022)
- [NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing](https://mlsanity.com/paper/2406.06523/) (2024)
- [Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time](https://mlsanity.com/paper/2309.14022/) (2023)
- [Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model](https://mlsanity.com/paper/2403.11077/) (2024)

## Abstract (arXiv)

Exploring and editing colors in images is a common task in graphic design and photography. However, allowing for interactive recoloring while preserving smooth color blends in the image remains a challenging problem. We present LayerBuilder, an algorithm that decomposes an image or video into a linear combination of colored layers to facilitate color-editing applications. These layers provide an interactive and intuitive means for manipulating individual colors. Our approach reduces color layer extraction to a fast iterative linear system. Layer Builder uses locally linear embedding, which represents pixels as linear combinations of their neighbors, to reduce the number of variables in the linear solve and extract layers that can better preserve color blending effects. We demonstrate our algorithm on recoloring a variety of images and videos, and show its overall effectiveness in recoloring quality and time complexity compared to previous approaches. We also show how this representation can benefit other applications, such as automatic recoloring suggestion, texture synthesis, and color-based filtering.