Customers inside a B&M store, which is one of more than 100 businesses that will be using the technology. Customers inside a B&M store, which is one of more than 100 businesses that will be using the technology. Civil liberties groups say Facewatch system in stores such as Sainsbury's and B&M is'dangerous escalation' Fri 10 Jul 2026 06.19 EDTLast modified on Fri 10 Jul 2026 06.57 Facial recognition technology in shops will soon alert police in real time to the presence of serious offenders, with civil liberties groups warning of a "dangerous escalation" towards surveillance and criminalisation in the retail sector. Facewatch, a facial recognition system used by more than 100 businesses including Sainsbury's, B&M and Spar to monitor thieves, said it was launching a UK-first feature to "alert police instantly when the most serious offenders trigger a live facial recognition match".

Bees seem to show when they are pleased and like something, rather than just needing it, in one of the strongest signs yet that insects have subjective experiences. In recent decades, it has become clear that bees are capable of more complex behaviours than we previously thought, such as counting and demonstrating a sense of rhythm . But discerning whether they have inner states akin to our emotions is more difficult. For one thing, insects don't have the flexible facial musculature of mammals, which we use to communicate our feelings. "How can we get any behavioural readout of these insects with a hard body and their mask of a face," asks Andrew Barron at Macquarie University in Sydney, Australia.

What Happened to Your Face? How the human countenance became something to study, edit, optimize, and scan. The physiognomists promised that your character could be read from your features. Certain forms of facial-recognition technology have revived that old fantasy in digital form. Several months ago, my partner and I bought an apartment in South London. Our previous home was a rental in which, for reasons best known to the landlord, there were mirrors everywhere. The bathroom had two; there was one outside on the terrace; in the bedroom, mirrored panels stretched across a twenty-foot-long wall. On moving day, we realized that we had a problem: the new apartment was mirror-free, and because we'd been so spoiled we weren't bringing one of our own. We spent a few days filling our drafty rooms, decanting books, building furniture, and dressing every morning without seeing ourselves in profile. It was a couple of weeks before we bought a simple mirror, wooden and round, to hang above the bathroom sink. By then, I joked, we didn't recognize ourselves.

The new Meta-branded glasses have the same camera, microphones, and chatbot as the Ray-Bans. They come in three styles, one of which was codesigned with Kylie Jenner. Smart glasses are like public transportation, according to Peter Bristol, Meta's vice president of industrial design. "People will use it when it's good enough." To reach "good enough," Meta is making its smart glasses more accessible, more customizable, and comfier to wear.

Synthesizing high-quality 3D head textures is crucial for gaming, virtual reality, and digital humans. Achieving seamless 360 textures typically requires expensive multi-view datasets with precise tracking. However, traditional methods struggle without back-view data or precise geometry, especially for human heads, where even minor inconsistencies disrupt realism. We introduce HairFree, an unsupervised texturing framework guided by textual descriptions and 2D diffusion priors, producing high-consistency 360 bald head textures--including non-human skin with fine details--without any texture, back-view, bald, non-human, or synthetic training data. We fine-tune a diffusion prior on a dataset of mostly frontal faces, conditioned on predicted 3D head geometry and face parsing.

Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time preprocessing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of preprocessing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection2.

We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at 256 256, into crystal-clear, photorealistic 4K outputs.

Faces synthesized by diffusion models (DMs) with high-quality and controllable attributes pose a significant challenge for Deepfake detection. Most state-of-the-art detectors only yield a binary decision, incapable of forgery localization, attribution of forgery methods, and providing analysis on the cause of forgeries. In this work, we integrate Multimodal Large Language Models (MLLMs) within DMbased face forensics, and propose a fine-grained analysis triad framework called VLForgery, that can 1) predict falsified facial images; 2) locate the falsified face regions subjected to partial synthesis; and 3) attribute the synthesis with specific generators. To achieve the above goals, we introduce VLF (Visual Language Forensics), a novel and diverse synthesis face dataset designed to facilitate rich interactions between'Visual' and'Language' modalities in MLLMs. Additionally, we propose an extrinsic knowledge-guided description method, termed EkCot, which leverages knowledge from the image generation pipeline to enable MLLMs to quickly capture image content. Furthermore, we introduce a low-level vision comparison pipeline designed to identify differential features between real and fake that MLLMs can inherently understand. These features are then incorporated into EkCot, enhancing its ability to analyze forgeries in a structured manner, following the sequence of detection, localization, and attribution. Extensive experiments demonstrate that VLForgery outperforms other state-of-the-art forensic approaches in detection accuracy, with additional potential for falsified region localization and attribution analysis.

This work introduces Clipout, a method for removing a target concept in pretrained text-to-image models. By randomly clipping units from the learned data embedding and using a contrastive objective, models are encouraged to differentiate these clipped embedding vectors.

We propose VASA-3D, an audio-driven, single-shot 3D head avatar generator. This research tackles two major challenges: capturing the subtle expression details present in real human faces, and reconstructing an intricate 3D head avatar from a single portrait image. To accurately model expression details, VASA-3D leverages the motion latent of VASA-1 [1], a method that yields exceptional realism and vividness in 2D talking heads. A critical element of our work is translating this motion latent to 3D, which is accomplished by devising a 3D head model that is conditioned on the motion latent. Customization of this model to a single image is achieved through an optimization framework that employs numerous video frames of the reference head synthesized from the input image. The optimization takes various training losses robust to artifacts and limited pose coverage in the generated training data. Our experiment shows that VASA-3D produces realistic 3D talking heads that cannot be achieved by prior art, and it supports the online generation of 512 512 free-viewpoint videos at up to 75 FPS, facilitating more immersive engagements with lifelike 3D avatars.