Robust Video Matting vs Transparent Background

Robust Video Matting is a free project for separating people from video backgrounds. It processes frames with a recurrent neural network that carries temporal memory, and the project says it can perform matting in real time without additional inputs. A browser webcam demo runs the model live and can visualize its recurrent states. The conversion API accepts a video or image sequence and can produce a composited video or PNG sequence, optionally including alpha and foreground outputs.

Models are available for PyTorch, TorchHub, TorchScript, ONNX, TensorFlow, TensorFlow.js, and CoreML. MobileNetv3 is recommended for most uses; ResNet50 is larger and described as offering small performance improvements. The README reports GPU throughput figures, including 4K at 76 FPS and HD at 104 FPS on a GTX 1080 Ti, and 4K at 154 FPS and HD at 172 FPS on an RTX 3090 with FP16 under specified settings. The project cautions that file conversion runs much slower because its script does not use hardware video encoding or decoding or parallel tensor transfers. CoreML models require iOS 13 or later and do not support dynamic resolution. The code is GPL-3.0 licensed.

Transparent Background is a free Python package that removes backgrounds from images and video using InSPyReNet. It accepts individual files, folders of images or videos, and webcam addresses through its command-line interface; it also offers a Python API and optional GUI. Outputs include transparent RGBA images, saliency maps, green or white backgrounds, blurred or overlaid backgrounds, and composites with another image. The available model modes are base, fast, and base-nightly, with support for custom checkpoints. Static resizing is the default and favors stability over detail; dynamic resizing may sharpen edges but can be unstable. The package supports batch processing and PNG, JPG/JPEG, and MP4 inputs. RGBA output is not supported for video or webcam input. Webcam support is optional, described as unstable, and limited to Linux. Installations are available from PyPI, GitHub, or a local clone, with CPU-only and GPU setup instructions. It runs on Linux, macOS, Windows, and self-hosted systems. The listed plan is free.