Open-source alternatives to Obs Backgroundremoval

Dot is a deep learning face swap tool used to replace faces in live video streams, recorded media, and static images. It functions as a deepfake media processor and real-time video manipulator that applies facial transformations through neural network mapping. The system includes a virtual camera video injector that routes processed output into a system-level virtual device to simulate a physical hardware webcam. This allows generated video to be used within third-party video conferencing software. The tool supports real-time source switching via keyboard inputs to toggle between different s

Perfect Green Screen Keys

RobustVideoMatting is a deep learning video matting tool and PyTorch library designed to remove backgrounds from videos and extract human subjects. It utilizes a temporal video segmentation model to ensure consistent matting and reduce flickering across video frames. The project includes a cross-platform model exporter that converts trained neural networks into various runtime formats. This allows for model deployment across multiple environments, including web and mobile applications. The framework provides capabilities for temporal video background removal and AI video post-production with

MODNet is a deep learning image segmenter and portrait matting model designed to isolate human subjects from backgrounds. It generates high-quality alpha mattes for images and video using only standard RGB input, removing the requirement for manual trimap guides. The framework is optimized for real-time inference and provides utilities to export pre-trained model weights into specialized formats for deployment on target hardware. The project covers the full workflow for portrait isolation, including supervised matting model training on labeled datasets, real-time video background removal, an

U-2-Net is a PyTorch image segmentation framework and computer vision saliency model designed to generate high-resolution foreground-background masks. It functions as an AI background removal tool that identifies and isolates the most visually prominent objects within an image. The model utilizes a nested U-structure design to detect salient objects, creating precise cutouts by predicting saliency maps. These capabilities enable the separation of main subjects from their surroundings to create transparent images. The framework covers several image processing workflows, including automatic ba

Backgroundremover is an AI-powered tool that removes backgrounds from both images and videos, accessible through a command-line interface and a Python API. At its core, it uses a pre-trained deep learning model to classify each pixel as foreground or background, producing a binary mask for removal. The tool distinguishes itself through multiple integration methods and output capabilities. It can process images and videos via Unix pipeline data streams, operate as an HTTP API server, or be called programmatically within Python scripts. Users can choose among different AI models to balance proc

h5player is an HTML5 video player extension and web media controller that adds advanced playback controls, visual filters, and media downloading capabilities to any web page using the HTML5 video tag. It functions as a customizable media hotkey manager and real-time video filter tool to enhance the standard browser viewing experience. The project is distinguished by its configuration-driven extension system, which allows for the remapping of playback shortcuts and the addition of new features through external scripts. It also provides a real-time visual filtering suite for modifying brightnes

lite.ai.toolkit is a C++ computer vision toolkit designed for edge AI deployment. It enables the execution of pre-trained models for object detection, image classification, and segmentation on resource-constrained devices. The project features a multi-backend inference engine that supports the ONNX model runtime, allowing AI models to run across different hardware targets. It includes a GPU-accelerated pipeline specifically for NVIDIA hardware to reduce latency and increase processing speed. The toolkit covers a broad range of facial analysis capabilities, including emotion detection, gender

This software is a computer vision utility designed for automated subject isolation and background removal. It provides a graphical desktop interface that allows users to extract foreground subjects from static images, video files, and live webcam streams without requiring command-line interaction. The application leverages deep learning models to generate high-fidelity alpha masks, enabling the creation of transparent backgrounds or the application of custom replacements. By utilizing hardware-accelerated tensor processing, the system performs real-time segmentation on live camera feeds and

LFLiveKit is an iOS live streaming SDK designed for capturing, encoding, and transmitting real-time audio and video streams over the RTMP protocol. The toolkit includes a hardware media encoder that uses H264 and AAC acceleration to compress streams, as well as a GPU-accelerated video filter for applying real-time beauty effects and watermarks. It also features an adaptive bitrate streamer that dynamically adjusts transmission rates and manages frame drops to maintain stability during network fluctuations. The system supports media capture from external peripheral devices and screen recordin

Yasea is an Android live streaming client used for broadcasting live camera and microphone feeds to remote servers via the RTMP protocol. It provides a system for mobile live broadcasting that integrates camera control, media encoding, and real-time network transmission. The project features GPU-accelerated video filtering to apply visual effects to live streams and a local recording system that saves a copy of the broadcast to an MP4 file while simultaneously streaming to a remote destination. It includes a camera controller for managing front and rear sensors and adjusting stream orientatio

HivisionIDPhotos is an AI-powered identification photo generator designed to automate the creation of standardized portraits. It utilizes machine learning to handle alignment, cropping, and background removal, transforming regular images into official identification photographs. The system features a background removal tool that uses offline inference to isolate subjects and a portrait enhancement tool that applies beauty filters to improve facial appearance and skin quality. To prepare photos for physical use, it includes a print layout generator that arranges processed images into standard

IOPaint is an AI image editor and Stable Diffusion inpainting tool providing a web interface for removing objects and replacing image content. It utilizes latent diffusion image processing to synthesize high-resolution replacements for erased sections of an image. The project features a specialized AI background remover for isolating subjects and an AI image upscaler that employs super-resolution models for general photos and anime artwork. The software covers a broad range of capabilities including image segmentation for object isolation, face restoration for improving facial details, and t

Faceai is a computer vision toolkit designed for facial analysis, identity recognition, and image processing. It provides integrated engines for detecting human faces in static images and live video streams, matching facial encodings against identity databases, and mapping facial landmarks to understand geometric structure and alignment. The project enables real-time augmented reality applications, such as applying virtual makeup and digital accessories by scaling assets to detected facial coordinates. It also includes a suite for digital image restoration capable of removing noise, erasing w

This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend the core OpenCV framework. It serves as a comprehensive library of extra modules for computer vision research, providing advanced toolsets for image processing, visual data analysis, and object detection. The library includes specialized frameworks for augmented reality tracking, biometric face recognition, and three-dimensional pose estimation. It provides distinct capabilities for identifying AR markers, tracking 3D object silhouettes, and performing neural network vulnerabili

This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r

Track-Anything is an AI-driven video object segmentation and tracking system. It utilizes the Segment Anything Model to isolate and mask multiple objects across video frames, providing tools for automated mask propagation and background-filling inpainting. The system distinguishes itself through a multi-object segmentation pipeline that can follow several distinct targets simultaneously. It includes a video inpainting utility to remove tracked objects and replace them with synthesized background content, as well as temporal mask refinement to correct tracking drift. The project covers broad

GPUImage2 is a Swift framework for applying real-time filters and effects to images and video using the GPU. It provides a real-time video filter library, an image geometry manipulation engine, and an OpenGL shading pipeline for processing visual data on graphics hardware. The framework enables the construction of visual effect pipelines by chaining image sources to consumers in sequential flows. It supports the development of custom fragment and vertex shaders for bespoke image processing and offers the ability to bundle these operations into reusable units via graph-based grouping. Capabil

BiRefNet is a PyTorch image segmentation framework designed for high-precision binary mask generation. It functions as a bilateral image segmentation model used to isolate foreground objects from complex backgrounds, as well as a specialized tool for camouflaged object detection and industrial defect detection. The project is designed for export to the ONNX format, which facilitates cross-platform deployment and inference. It supports custom model fine-tuning on user-provided image and mask datasets to adapt the model for specialized professional use cases. The system covers high-resolution

Real-Time-Person-Removal is a web-based computer vision application designed to identify and remove human figures from live video streams. Using TensorFlow.js, the tool functions as a real-time background subtraction system that analyzes scene composition to isolate static backgrounds from moving people. The project enables browser-based computer vision by processing webcam video feeds directly in the client. It utilizes machine learning to differentiate between dynamic scene elements and the background, allowing for the real-time removal of people from the visual field.

StreamFX-Public is a collection of OBS Studio plugins and software components designed for broadcast enhancement. It provides a suite of visual effects, filters, hardware-accelerated encoders, and professional video encoding tools to expand the capabilities of the host application. The project distinguishes itself through the support of professional mezzanine video codecs, including DNxHR, ProRes, and Cineform, for high-fidelity post-production editing. It also implements hardware-accelerated recording via GPU cores and AMD AMF to reduce CPU overhead during live broadcasts. The toolset cover

MedSAM is a deep learning framework designed for automating the segmentation of anatomical structures in 2D and 3D medical imagery. It provides specialized tools for fine-tuning pretrained segmentation weights on custom medical datasets and evaluating the accuracy of those predictions against ground truth labels. The project focuses on adapting the Segment Anything Model architecture for medical use, enabling the isolation of specific anatomical structures through prompt-guided methods such as bounding boxes and point prompts. The system covers a full medical AI workflow, including data engi

Rembg is a machine learning-based toolkit designed for automated image background removal and subject segmentation. It functions as a versatile engine that identifies and extracts subjects from images, supporting diverse input methods including individual files, directory-based batch processing, and live binary data streams. The project distinguishes itself through its flexible integration options, offering a command-line interface for local automation, a library for programmatic access, and an HTTP service for remote requests. It utilizes deep learning architectures to classify pixels and ge

FastSAM is an image segmentation framework that uses convolutional neural networks to isolate visual elements and generate masks for detectable objects within images. It provides a system for both automatic all-object segmentation and promptable image segmentation. The project utilizes an inference-optimized architecture to reduce computational overhead, enabling faster mask generation and real-time visual analysis. It supports the creation of precise masks through various prompt inputs, including points, bounding boxes, and text descriptions. The framework covers broader computer vision cap

BackgroundMattingV2 is a deep learning background matting tool and real-time image segmentation framework. It provides a system for isolating foreground subjects from high-resolution images and video feeds in real time. The project includes a deep learning model trainer for optimizing matting models through base convergence and end-to-end refinement. It also functions as a cross-runtime model exporter, converting trained neural networks into interchangeable formats for deployment across different software environments and hardware runtimes. The framework supports streaming processed webcam f

This project is a macOS system camera driver and software plugin that exposes software video streams as hardware-recognized camera inputs. It functions as an OBS virtual camera plugin, allowing the live output of OBS to be utilized as a webcam device within other applications. The tool enables the routing of composited video from a production suite into video conferencing applications such as Zoom or Google Meet. This allows for the streaming of processed scenes instead of a raw webcam feed. The system integrates with macOS using a kernel-level device driver and shared-memory buffer transfer