Video face restoration (VFR) aims to recover high-quality and temporally consistent facial details from severely degraded video sequences; however, existing methods still struggle to balance spatial fidelity and temporal coherence under complex degradations. To address this, we propose FADRA, a frequency-aware diffusion framework with iterative residual adaptation specifically tailored for robust VFR.
arXiv:2505.23462v2 Announce Type: replace
Abstract: Blind face restoration from low-quality images is a challenging task that requires not only high-fidelity image reconstruction, but also preservati...
By Runyi Li, Bin Chen, Jian Zhang, Radu Timofte
Face Video Restoration (FVR) aims to recover high-fidelity facial videos from degraded input while preserving identity and semantic consistency across frames. Existing methods often struggle to simultaneously address three key challenges: identity shift, viewpoint-entangled guidance, and perceptual realism.
The paper introduces EC²Face, a multimodal face synthesis framework that enhances semantic alignment by combining Explicit Conditional Consistency Guidance (ECCG) and Long‑Tail Adaptive Flow Matching (LAFM). ECCG enforces pixel‑level consistency between generated faces, textual descriptions, and semantic masks, while a temporal dynamic modulation adjusts supervision strength over diffusion timesteps. LAFM reweights spatial optimization signals according to attribute frequency, improving rare attribute synthesis without adding inference overhead. Experiments demonstrate that EC²Face outperforms baselines, achieving a 29.38% improvement in mask accuracy for rare attributes.
By Yushe Cao, Xuechao Zou, Xing Xi, Dianxi Shi, Chun Yu, Junliang Xing
arXiv:2608. 20212v1 Announce Type: new Abstract: High-fidelity removal of eyeglasses from video is a major challenge in facial attribute editing, as the underlying facial geometry is often obscured by complex refractive distortions and view-dependent specular reflections.
By Radim Spetlik, David Futschik, Radek Danecek, Feitong Tan, Ziqian Bai, Rohit Pandey, Yinda Zhang
arXiv:2608. 09133v1 Announce Type: cross Abstract: Image super-resolution (SR) with large generative models has recently achieved remarkable perceptual quality, yet maintaining fidelity to the LR observation remains challenging.
By Yu Shi, Yuyao Zhang, Yu-wing Tai
The paper introduces MiRA, a plug‑in framework that reweights framewise attention in Vision Transformer video models to better capture subtle facial dynamics for expression recognition. MiRA computes frame‑level confidence and intra‑frame concentration from self‑attention maps, redistributing attention toward localized facial cues without adding trainable parameters. Two modes—an exact post‑softmax redistribution and a lightweight flashLite pre‑softmax approximation—are proposed, and experiments on facial expression recognition benchmarks show consistent gains over strong ViT baselines.
By Seongro Yoon, Donghyeon Cho, Jinsun Park, Fran\c{c}ois Br\'emond
arXiv:2603.17825v2 Announce Type: replace
Abstract: In this work, we study the role of Massive Activations (MAs), which are rare, high-magnitude spikes confined to a few fixed hidden dimensions in vi...
By Xianhang Cheng, Yujian Zheng, Zhenyu Xie, Tingting Liao, Hao Li
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
HonestFace is a one‑step diffusion model designed for honest face restoration, preserving identity and realistic textures. It introduces an identity embedder that captures key identity features from low‑quality inputs and reference faces, and a masked face alignment method to enhance fine‑grained details. The authors also release a new real‑world dataset, MultiRefCeleb‑Test, and demonstrate that HonestFace outperforms state‑of‑the‑art methods in both visual quality and quantitative metrics.
By Jingkai Wang, Wu Miao, Jue Gong, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu, Yulun Zhang
arXiv:2512. 04390v2 Announce Type: replace-cross Abstract: Joint video super-resolution and deblurring (VSRDB) requires both efficient long-range temporal modeling and robustness to frame-wise exposure-duration variation, which changes the extent of motion blur across video frames.
By Geunhyuk Youk, Jihyong Oh, Munchurl Kim
arXiv:2602.24289v2 Announce Type: replace
Abstract: Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-f...
By Shengqu Cai, Weili Nie, Chao Liu, Julius Berner, Lvmin Zhang, Nanye Ma, Hansheng Chen, Maneesh Agrawala, Leonidas Guibas, Gordon Wetzstein, Arash Vahdat