FG-DFPN: Flow Guided Deformable Frame Prediction Network

• URL: http://arxiv.org/abs/2503.11343v1
• Date: Fri, 14 Mar 2025 12:18:33 GMT
• Title: FG-DFPN: Flow Guided Deformable Frame Prediction Network
• Authors: M. Akın Yılmaz, Ahmet Bilican, A. Murat Tekalp,
• Abstract summary: We present FG-DFPN, a novel architecture that harnesses the synergy between optical flow estimation and deformable convolutions to model complex dynamics.<n>Our experiments demonstrate that FG-DFPN achieves state-of-the-art performance on eight diverse MPEG test sequences, outperforming existing methods by 1 PSNR while maintaining competitive inference speeds.
• Score: 5.6390038395163815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video frame prediction remains a fundamental challenge in computer vision with direct implications for autonomous systems, video compression, and media synthesis. We present FG-DFPN, a novel architecture that harnesses the synergy between optical flow estimation and deformable convolutions to model complex spatio-temporal dynamics. By guiding deformable sampling with motion cues, our approach addresses the limitations of fixed-kernel networks when handling diverse motion patterns. The multi-scale design enables FG-DFPN to simultaneously capture global scene transformations and local object movements with remarkable precision. Our experiments demonstrate that FG-DFPN achieves state-of-the-art performance on eight diverse MPEG test sequences, outperforming existing methods by 1dB PSNR while maintaining competitive inference speeds. The integration of motion cues with adaptive geometric transformations makes FG-DFPN a promising solution for next-generation video processing systems that require high-fidelity temporal predictions. The model and instructions to reproduce our results will be released at: https://github.com/KUIS-AI-Tekalp-Research Group/frame-prediction

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