LiVES Video Editing System
LiVES is an open-source video editing system designed for both professional and amateur video editors. It supports real-time and non-linear editing, a wide range of video formats, and includes features like frame accurate cutting, real-time effects, and support for various audio
Link
Related resources
- BackgroundMattingV2Real-time high-resolution background matting requiring a captured background plate, achieving 4K 30fps on RTX 2080 Ti, companion to arXiv:2…
- RobustVideoMattingReal-time human video matting via recurrent neural network requiring no green screen, achieving 4K 76FPS on GTX 1080 Ti, companion to arXiv…
- SparseSync: Audio-Visual Synchronisation with Trainable SelectorsBMVC 2022 Spotlight paper and code for sparse-in-space-and-time audio-visual sync detection.
- 5G QoE Prediction: ML-based Quality-Shift Prediction for Video StreamingMachine-learning quality-shift prediction for video streaming over 5G networks, includes 1-sec granularity CLM/YouTube QoE dataset, compani…
- VQMTK: Video Quality Metrics ToolkitOpen-source cross-platform tool bundling 14 VQA metrics plus SI/TI spatial/temporal indicators, CLI or Jupyter web UI.
- Evaluating Video Quality Metrics for Neural and Traditional Codecs (4K/UHD-1)Academic comparison of VMAF, AVQBits, and FasterVQA no-reference quality metrics on 4K content.
- Bitrate Ladder Construction via Transfer Learning and Spatio-Temporal FeaturesML-based bitrate ladder optimization achieving 94.1% complexity reduction at only 1.71% BD-rate cost using transfer learning.
- VMAF-torchPyTorch reimplementation of VMAF metric, GPU/gradient-friendly for use in learned codec optimization loops.
- CompressedVQA: Full/No-Reference VQA for Compressed UGC VideoICME 2021 grand challenge-winning full-reference and no-reference video quality models for compressed user-generated content.
- 2BiVQA: Double Bi-LSTM No-Reference Video Quality AssessmentNo-reference VQA model for UGC video using double Bi-LSTM, companion code to arXiv:2208.14774.