Flow 1080p May 2026

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: A Framework for Real-Time High-Definition Optical Flow Estimation and Visualization 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 | Optical flow estimation remains a cornerstone of computer