Background Eraser vs Robust Video Matting

Background Eraser removes or replaces video backgrounds during recording or from imported footage, and removes photo backgrounds in one click. GPU-accelerated AI processes video and images on the device, without uploading footage; the app also works offline and requires no account. Video backgrounds can be replaced with colors, images, looping videos, blur or studio presets, with controls for feathering, smoothing, tightness and temporal smoothing. Video detection is designed for people in talking-head videos, presentations and tutorials. For photos, users can choose which detected subjects to keep. Videos export as MP4 H.264 at full, 1080p, 720p or 540p; photo cutouts export as PNG, including transparent PNG. Canvas presets include 9:16, 1:1, 4:5, 16:9 and other social formats. The free plan includes recording and editing, but adds a watermark to video and photo exports. Pro removes watermarks through weekly, monthly or yearly auto-renewing subscriptions; current prices are shown in the app.

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.