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Flow 1080p: A Framework for Real-Time High-Definition Optical Flow Estimation and Visualization

Optical flow estimation remains a cornerstone of computer vision, yet achieving dense, accurate flow fields at full HD resolution (1080p) in real time presents significant computational challenges. This paper introduces Flow 1080p , a novel hybrid architecture combining sparse feature matching with learned upsampling to generate 1920×1080 pixel flow fields at ≥30 FPS on consumer hardware. We demonstrate applications in real-time video interpolation, motion segmentation, and artistic flow visualization. Our method reduces memory bandwidth by 62% compared to dense full-resolution methods while maintaining endpoint error below 0.3 pixels on standard benchmarks. flow 1080p

| Method | Resolution | FPS | Endpoint Error | Memory (GB/frame) | |----------------|------------|------|----------------|-------------------| | RAFT (iter=20) | 1080p | 9 | 0.21 | 2.8 | | Farneback | 1080p | 14 | 0.67 | 1.1 | | | 1080p | 34 | 0.29 | 0.9 | Our method reduces memory bandwidth by 62% compared

Traditional optical flow algorithms (e.g., Farneback, DeepFlow, RAFT) optimize for either accuracy or speed. HD resolution (1080p) exacerbates the trade-off: dense per-pixel computation leads to latency >200 ms on GPUs. Flow 1080p redefines the problem by operating on a multiscale pyramid where full resolution is reserved for boundary refinement. The name reflects both the target resolution and the "flow" of visual information across frames. Flow 1080p redefines the problem by operating on

(For illustrative purposes) J. Chen, M. Rivera, T. Aoki Institute for Computational Imaging & Media Dynamics