arXiv Computer Vision
Oct 12

High-Throughput Generative Video Super-Resolution on a Single Consumer GPU

The paper presents a zero‑shot framework for high‑throughput generative video super‑resolution (VSR) that operates on a single high‑end consumer GPU without retraining. It decouples spatial upscaling, handled by a time‑distilled one‑step diffusion backbone, from temporal stabilization, achieved with a lightweight pretrained flow‑based interpolator. A resolution‑aware hybrid attention scheme replaces high‑resolution global attention with neighborhood attention to improve scalability, achieving up to ~32 FPS at 720p and a 44% speedup at 4K on an NVIDIA RTX 4090, while maintaining competitive perceptual quality and temporal consistency on standard VSR benchmarks.

By Georgios Panaretou, Sotirios Chatzis
arXiv Computer Vision
Oct 12

Leveraging Motion Estimation for Efficient Bayer-Domain Video Convolutional Networks

The paper introduces Motion Estimation-based Video Convolution (MEVC), a framework that removes the need for image signal processors by operating directly on raw Bayer pixel data. MEVC incorporates sliding‑window motion estimation into each convolutional layer, allowing prediction and residual refinement to cut redundant computations across frames. The resulting end‑to‑end pipeline achieves more than 70% reduction in FLOPs while maintaining accuracy on video semantic segmentation, depth estimation, and object detection tasks.

By Haichao Wang, Jiangtao Wen, Yuxing Han