arXiv Computer Vision
Oct 5

Active Sampling for Ultra-Low-Bit-Rate Video Compression via Conditional Controlled Diffusion

ActDiff-VC is a diffusion-based video compression framework designed for ultra‑low‑bitrate scenarios. It partitions videos into variable‑length segments, transmits keyframes only when necessary, and encodes temporal dynamics with a compact set of tracked point trajectories. Conditioned on these sparse signals, a conditional diffusion decoder reconstructs the remaining frames, achieving significant bitrate reductions and perceptual quality gains compared to strong learned codecs.

By Amirhosein Javadi, Shirin Saeedi Bidokhti, Tara Javidi
arXiv Computer Vision
Sep 30

FastVR: Efficient Streaming Video Restoration with One-Step Diffusion

FastVR is a streaming video restoration framework that uses a one‑step diffusion model to achieve strong restoration quality and temporal consistency while processing 1080p video at 11 FPS on a single H20 GPU. It addresses efficiency bottlenecks by combining a lightweight VAE with chunk‑wise causal attention, and improves inference speed and restoration quality through velocity consistency regularization and continuous trajectory learning during training. Experiments demonstrate that FastVR outperforms diffusion baselines in efficiency and achieves state‑of‑the‑art performance on both synthetic and real‑world benchmarks.

By Xiaoxu Chen, Qin Yang, Haoran Bai, Sibin Deng, Ying Chen