通过高效适配扩散Transformer实现反射去除 / Reflection Removal through Efficient Adaptation of Diffusion Transformers
1️⃣ 一句话总结
这项研究提出了一种新方法，通过高效微调一个预先训练好的扩散Transformer大模型，并结合逼真的合成数据，来智能地去除单张照片中由玻璃等表面产生的恼人反光，效果达到了当前最佳水平。
🤖 系统
Abstract - Reflection Removal through Efficient Adaptation of Diffusion Transformers
We introduce a diffusion-transformer (DiT) framework for single-image reflection removal that leverages the generalization strengths of foundation diffusion models in the restoration setting. Rather than relying on task-specific architectures, we repurpose a pre-trained DiT-based foundation model by conditioning it on reflection-contaminated inputs and guiding it toward clean transmission layers. We systematically analyze existing reflection removal data sources for diversity, scalability, and photorealism. To address the shortage of suitable data, we construct a physically based rendering (PBR) pipeline in Blender, built around the Principled BSDF, to synthesize realistic glass materials and reflection effects. Efficient LoRA-based adaptation of the foundation model, combined with the proposed synthetic data, achieves state-of-the-art performance on in-domain and zero-shot benchmarks. These results demonstrate that pretrained diffusion transformers, when paired with physically grounded data synthesis and efficient adaptation, offer a scalable and high-fidelity solution for reflection removal. Project page: this https URL