CleanVideo introduces a selective erasure framework for text-to-video diffusion models, addressing the challenge of removing undesired visual concepts from videos. The method uses a low-dimensional subspace intervention guided by a tri-modal gating mechanism that jointly considers spatiotemporal visual features, timestep signals, and textual semantics to decide where, when, and whether to intervene. Experiments on three video diffusion models demonstrate that CleanVideo effectively erases target concepts while preserving visual fidelity, temporal coherence, and outperforming existing baselines in both frame-level and video-level evaluations, even under concept-recovery attacks.
By Junchi Liao, Hongji Li, Wenrui Zhou, Lijie Hu
ContextAnyone is a context‑aware diffusion framework that treats a reference image as an explicitly preserved appearance anchor rather than a simple conditioning signal. By jointly reconstructing the reference image and generating the target video within a shared diffusion transformer, it provides direct supervision for maintaining identity and fine‑grained appearance throughout denoising. The method introduces asymmetric information flow and Gap‑RoPE positional representations to keep the reference stable while allowing selective access by video tokens, and demonstrates improved identity and appearance consistency on an OpenVid‑HD benchmark.
By Ziyang Mai, Yu-Wing Tai
arXiv:2608.29243v1 Announce Type: new
Abstract: Existing diffusion-based enhancement methods provide strong generative capability for low-light image enhancement (LLIE), yet they either rely on paire...
By Wenjie Cai, Yuezhe Yang, Jianyang Xia, Xingbo Dong, Zhe Jin
TOLA is a diffusion‑based text image super‑resolution method that eliminates iterative image‑text diffusion by using a one‑step latent adaptation framework. It employs a confidence‑weighted text conditioning module to build a reliable semantic condition and a lightweight latent residual correction module to fix structured residual errors, thereby preserving text fidelity. Experiments show TOLA outperforms existing diffusion‑based TSR methods, achieving at least 2.72 dB higher PSNR on the CTR‑TSR‑Test benchmark.
By Yike Xu, Yue Shi, Yong Guo, Jiezhang Cao
Diffusion-based video restoration recovers realistic details, but its practical deployment is limited by two efficiency bottlenecks: costly VAE encoding and decoding, and the quadratic cost of full se...
Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene, and generate repla...