OrienDiffusion: Taming Diffusion Towards Fine-grained Portrait Matting

Abstract

Recently, diffusion models have been applied to reformulate the regressive portrait matting task as a gradual denoising-driven generative process, alleviating the inherent limitations of discriminative models via iterative error correction. However, due to the lack of high-quality ground-truth mattes, existing generative methods for portrait matting still suffer from severe detail degradation, especially in structurally delicate hair strands and pixels with ambiguous colors and context. To address these challenges, we propose OrienDiffusion, a diffusion-based framework tailored for fine-grained portrait matting. Specifically, we construct a Hair Orientation Field to conditionally guide the denoising process, constraining the generation toward authentic structural consistency in hair regions. Furthermore, instead of relying on overly encoded deep features or ambiguous original color cues as existing methods do, we develop a Pixel Color-anchored Local Structure Embedding approach, which models alpha matte transitions as local structural variations, achieving accurate alpha estimation in pixels with ambiguous colors or context. Extensive experiments on multiple portrait matting datasets demonstrate that OrienDiffusion achieves state-of-the-art performance in these matting challenging regions. Code is available at https://github.com/xtz0001/OrienDiffusion.