Core Concepts
Zippo presents a unified framework for zipping color and transparency distributions into a single diffusion model, enabling efficient image generation.
Abstract
Abstract:
Recent studies explore adapting text-to-image diffusion models for visual perception tasks.
Zippo introduces a unified framework to generate RGB images from alpha mattes and predict transparency from input images.
Introduction:
Image generation advancements due to diffusion models.
Motivation to utilize generative diffusion models for image matting tasks.
Method:
Adapting T2I diffusion model for alpha mask estimation.
Incorporating Matte-to-RGB branch for colorful semantics.
Experiment:
Extensive experiments on AM2K and VITON-HD datasets showcase Zippo's capabilities in generating transparent images efficiently.
Related Work:
Comparison with existing methods like PPMatte, ControlNet, and DiffuMask.
Stats
ZippoはRGB画像を生成し、透明度を予測するための統合フレームワークを導入します。