FILM: Frame Interpolation for Large Motion

Description

Google Research's unified neural network for video frame interpolation handling large motion without requiring optical flow or depth estimation.

URL

• Visit resource

Related resources

• DQ-Ladder: Deep Q-Network Time/Quality-Aware Bitrate LadderDeep reinforcement learning (DQN) approach to bitrate ladder construction achieving 10.3%+ BD-rate gain and 22% decode-time reduction.
• Deep Reinforced Bitrate Ladders (DeepLadder)NOSSDAV 2021 paper on deep-reinforcement-learning-based bitrate ladder construction for adaptive streaming.
• 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.
• Efficient Bitrate Ladder Construction for Content-Optimized Adaptive StreamingAcademic paper (Katsenou et al.) using ML to predict Pareto-optimal bitrate ladders from spatio-temporal content features.
• AI-Youtube-Shorts-GeneratorLLM-driven highlight detection with Whisper transcription and automatic vertical cropping to turn long videos into shorts, MIT-licensed wit…
• 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.
• nr-vqa-consumervideoNo-reference video quality metric tuned for consumer video capture artifacts (sensor noise, motion blur, shake) rather than compression art…
• 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.