We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU. Our technique is based on background matting, where an additional frame of the background is captured and used in recovering the alpha matte and the foreground layer. The main challenge is to compute a high-quality alpha matte, preserving strand-level hair details, while processing high-resolution images in real-time. To achieve this goal, we employ two neural networks; a base network computes a low-resolution result which is refined by a second network operating at high-resolution on selective patches. We introduce two largescale video and image matting datasets: VideoMatte240K and PhotoMatte13K/85. Our approach yields higher quality results compared to the previous state-of-the-art in background matting, while simultaneously yielding a dramatic boost in both speed and resolution.
翻译:我们引入了实时高分辨率背景替代技术,在4K分辨率的30英尺和60英尺的30英尺的温度上操作,在现代GPU上操作高分辨率。我们的技术基于背景交配,在恢复阿尔法藻和前景层时捕获和使用额外的背景框架。我们的主要挑战是如何计算高品质的阿尔法藻,保存线性头发细节,同时实时处理高分辨率图像。为了实现这一目标,我们使用两个神经网络;一个基础网络计算低分辨率结果,由在选择性补丁上以高分辨率操作的第二个网络加以改进。我们引入了两个大型视频和图像交配数据集:VideMotte240K和PhotoMatte13K/85。我们的方法产生比前一个背景交配时的状态更高的质量结果,同时在速度和分辨率上带来巨大的加速。