Hippo Video SDK

Hippo Video SDK provides a complete video infrastructure that can be integrated into platforms with minimal code. It supports rapid deployment of video experiences, enabling developers to enhance their applications with video capabilities efficiently.

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Related resources

• GHVQ: Benchmark and Metric for AI-Generated Human-Activity Video QualityAcademic paper introducing a benchmark dataset and objective quality metric for AI-generated human-activity video, with companion code.
• VQA² (Visual Question Answering for Video Quality Assessment)ACM MM 2025 paper and code for VQA²-Assistant, a unified video/image quality scoring and interpretation model.
• SparseSync: Audio-Visual Synchronisation with Trainable SelectorsBMVC 2022 Spotlight paper and code for sparse-in-space-and-time audio-visual sync detection.
• AVT-VQDB-UHD-1-NVCDataset and code for 4K/UHD neural-video-codec quality evaluation, comparing VMAF, AVQBits, FasterVQA, CVQA-FR, LPIPS metrics.
• KVQ: NTIRE 2024 Short-form UGC Video Quality Assessment ChallengeCVPR NTIRE 2024 challenge dataset and code for short-form UGC video quality assessment, 600 videos/3600 processed clips.
• 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.
• 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.