Approximate Fast Foreground Colour Estimation

Abstract

Compositing objects from one image or video onto another background is a classic computer vision problem. Compositing requires an estimate of the foreground object's transparency, a topic of study known as Alpha Matting. However, less studied is the requirement of an estimate for the foreground object's colours in transparent regions. Naively compositing without this leads to bleed-through of the original background. In this work, we propose an approximation for the multi-level foreground estimation technique from Germer et al. [1]. Our method can be implemented 11 lines of Python code and yields comparable results to state of the art approaches while also being faster. Our code is available at https://github.com/PhotoRoom/ fast-foreground-estimation.

Self-Supervised Matting-Specific Portrait Enhancement and Generation

“…2. For the variants (FG w/ [13]) and (FG w/ [12]), they report similar results to the complete model (w * , n * ), which indicates that our method is not sensitive to the foreground estimation methods. The variant (FG w/ GT α) presents a similar performance with foreground estimation using the enhanced matte (w * , n * ).…”

“…We first compare with six variants: 1) optimizing the latent code w only while keeping the noise code n fixed (w * , n); 2) optimizing the noise code n only (w, n * ); 3) without the entropy #2). Furthermore, for evaluating the influence of different foreground estimation methods, we use MLFgE [13] and Blur-Fusion [12] instead of Closed-form Matting [27] for the foreground estimation respectively, resulting in two variants: 9) FG w/ [13] and 10) FG w/ [12]. 11) We also use the GT matte instead of enhanced matte for the foreground estimation used in Eq.…”