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: A Framework for Real-Time High-Definition Optical Flow Estimation and Visualization flow 1080p
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. Traditional optical flow algorithms (e
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