AV1 Motion Vector Fidelity and Application for Efficient Optical Flow

2025-10-20

AI Summary

Traditional optical flow estimation suffers from high computational cost, hindering real-time deployment. Method: This work presents the first systematic evaluation of motion vectors (MVs) extracted from AV1 and HEVC encoders in terms of fidelity to ground-truth optical flow, revealing that AV1 MVs achieve a superior trade-off between accuracy and sparsity. Leveraging this insight, we propose initializing the RAFT optical flow network with AV1 MVs—termed “warm-start initialization”—instead of random initialization. Contribution/Results: Our approach accelerates convergence and achieves ~4× inference speedup across multiple benchmarks, with only marginal endpoint error degradation (+0.03–0.08 px). We further provide empirically optimized AV1 encoding parameters for optical flow–assisted tasks. This work establishes a novel paradigm for efficient motion estimation leveraging compressed-domain priors, enabling real-time video understanding and action analysis.

Technology Category

• Computer Vision: Motion & Tracking
• Search and Optimization: Heuristic Search
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper presents a comprehensive analysis of motion vectors extracted from AV1-encoded video streams and their application in accelerating optical flow estimation. We demonstrate that motion vectors from AV1 video codec can serve as a high-quality and computationally efficient substitute for traditional optical flow, a critical but often resource-intensive component in many computer vision pipelines. Our primary contributions are twofold. First, we provide a detailed comparison of motion vectors from both AV1 and HEVC against ground-truth optical flow, establishing their fidelity. In particular we show the impact of encoder settings on motion estimation fidelity and make recommendations about the optimal settings. Second, we show that using these extracted AV1 motion vectors as a "warm-start" for a state-of-the-art deep learning-based optical flow method, RAFT, significantly reduces the time to convergence while achieving comparable accuracy. Specifically, we observe a four-fold speedup in computation time with only a minor trade- off in end-point error. These findings underscore the potential of reusing motion vectors from compressed video as a practical and efficient method for a wide range of motion-aware computer vision applications.

Problem

• Analyzing AV1 motion vectors for optical flow estimation
• Evaluating encoder settings impact on motion estimation fidelity
• Using AV1 motion vectors to accelerate RAFT convergence

Innovation

• AV1 motion vectors replace optical flow
• Warm-start RAFT with AV1 vectors
• Four-fold speedup with minor accuracy loss