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.
arXiv:2511.05575v2 Announce Type: replace Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based meth...
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...
arXiv:2605. 02814v2 Announce Type: replace-cross Abstract: Severe face degradation can remove person-specific evidence, making restoration underdetermined.
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.
arXiv:2609.27011v1 Announce Type: new Abstract: Target-oriented face de-identification models aim to anonymize the identity of a target individual across different images or video frames, such that t...
arXiv:2606. 20094v1 Announce Type: cross Abstract: Makeup transfer models enable fun augmented reality (AR) experiences as well as virtual try-on (VTO) for online makeup shopping.
The paper introduces VeriFi, a watermarking framework that protects face images from AI‑generated manipulation. It embeds a compact semantic latent watermark to preserve content, localizes pixel‑level edits without explicit payloads, and simulates realistic deepfake attacks to improve robustness. Experiments on CelebA‑HQ and FFHQ show that VeriFi outperforms existing methods in robustness, localization accuracy, and recovery quality.
arXiv:2608.23984v1 Announce Type: new Abstract: Recent advances in single-image 3D Gaussian head reconstruction have enabled highly realistic and freely renderable digital heads from a single portrai...
FADRA is a frequency‑aware diffusion framework designed for video face restoration (VFR). It combines a pre‑trained text‑to‑video diffusion model with lightweight LoRA adapters and a Low‑Quality Pixel‑Alignment Feature Fusion module to adapt the generative prior to VFR. The method introduces a Repeated Residual Adaptation Head that refines residuals step‑wise using low‑quality latent cues, and employs a Frequency‑Aware Loss to supervise multiple spectral bands, improving facial detail recovery and temporal consistency.
arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.
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:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.