We introduce Magenta Green Screen, a novel machine learning--enabled matting technique for recording the color image of a foreground actor and a simultaneous high-quality alpha channel without requiring a special camera or manual keying techniques. We record the actor on a green background but light them with only red and blue foreground lighting. In this configuration, the green channel shows the actor silhouetted against a bright, even background, which can be used directly as a holdout matte, the inverse of the actor's alpha channel. We then restore the green channel of the foreground using a machine learning colorization technique. We train the colorization model with an example sequence of the actor lit by white lighting, yielding convincing and temporally stable colorization results. We further show that time-multiplexing the lighting between Magenta Green Screen and Green Magenta Screen allows the technique to be practiced under what appears to be mostly normal lighting. We demonstrate that our technique yields high-quality compositing results when implemented on a modern LED virtual production stage. The alpha channel data obtainable with our technique can provide significantly higher quality training data for natural image matting algorithms to support future ML matting research.

翻译：我们提出了一种名为“品红绿幕”（Magenta Green Screen）的新型机器学习抠图技术，可在无需专业摄像机或手动抠像的情况下，同步录制前景演员的彩色图像与高质量alpha通道。该方法将演员置于绿色背景前，但仅使用红色与蓝色前景光源进行照明。在此配置下，绿色通道呈现出演员在明亮均匀背景上的剪影，可直接作为保留遮片（即演员alpha通道的逆通道）。随后，我们采用机器学习颜色化技术对前景的绿色通道进行修复。通过使用演员在白色光照下的示例序列训练颜色化模型，可获得令人信服且时间稳定的颜色化结果。进一步研究表明，在“品红绿幕”与“绿幕品红”之间进行时间复用照明，可使该技术在实际接近正常光照条件下运行。我们在现代LED虚拟制作舞台上验证了该方法能生成高质量的合成结果。该技术获取的alpha通道数据可为自然图像抠图算法提供显著更优质的训练数据，从而支持未来基于机器学习的抠图研究。