AI RESEARCH
Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion
arXiv CS.CV
•
ArXi:2605.02849v1 Announce Type: new Diffusion models provide a powerful generative prior for perceptual reconstruction at ultra-low bitrates, but effective video compression requires controlling the generative process using highly compact conditioning signals. In this work, we present ActDiff-VC, a diffusion-based video compression framework for the ultra-low-bitrate regime. Our method partitions videos into variable-length segments, transmits keyframes only when needed, and summarizes temporal dynamics using a compact set of tracked point trajectories.