Digital blackface flourishes under Trump and AI: 'The state is bending reality' Late last year, as a US government shutdown cut off the Snap benefits that low-income families rely on for groceries, videos on social media cast the fallout in frantic scenes. "Imma keep it real with you," a Black woman said in a viral TikTok post, "I get over $2,500 a month in stamps. I sell'em, $2,000 worth, for about $1,200-$1,500 cash." Another Black woman ranted about taxpayers' responsibility to her seven children with seven men, and yet another melted down after her food stamps were rejected at a corn-dog counter. Visible watermarks stamped some videos as AI-generated - apparently, too faintly for the racist commentators and hustlers more than happy to believe the frenzy was real.

Table 2d summarizes the effect of hand pose condition in discriminator.

Recent advances in 3D-aware GAN models have enabled the generation of realistic and controllable human body images. However, existing methods focus on the control of major body joints, neglecting the manipulation of expressive attributes, such as facial expressions, jaw poses, hand poses, and so on.

A British AI startup that makes realistic video avatars has almost doubled its valuation to $4bn (£3bn), in a boost for the UK technology sector. Synthesia was valued at $2.1bn last year and moved into new offices in central London, marking the moment with a ceremony attended by the Sadiq Khan, the city's mayor, and Peter Kyle, then technology secretary. On Monday, it announced its latest funding round, led by an existing investor, Google Ventures, had raised $200m and valued the British company at $4bn. Google Ventures is the search firm's venture capital arm. Synthesia uses human actors to generate digital avatars of people and also offers employers the ability to create replicas of their staff.

Hollywood has revealed the nominations for this year's Oscars, which will honour the film industry's finest stars and movies from the past 12 months. Sinners leads the way with a record 16 nominations, breaking the record for the most Oscar nominations, which was previously held by All About Eve (1950), Titanic (1997) and La La Land (2016). One Battle After Another is next with 13 nominations, while Marty Supreme, Frankenstein and Sentimental Value are next with nine, and Hamnet has eight. The awards will take place on 15 March, hosted by US comedian Conan O'Brien. Rose Byrne - If I Had Legs I'd Kick You Read more about this year's nominated films: Could Oscar glory be next for Jessie Buckley and Hamnet?

Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developing prompting techniques that enable LLM agents to effectively use these tools and knowledge remains a heuristic and labor-intensive task. Here, we introduce AvaTaR, a novel and automated framework that optimizes an LLM agent to effectively leverage provided tools, improving performance on a given task. During optimization, we design a comparator module to iteratively deliver insightful and comprehensive prompts to the LLM agent by contrastively reasoning between positive and negative examples sampled from training data. We demonstrate AvaTaR on four complex multimodal retrieval datasets featuring textual, visual, and relational information, and three general question-answering (QA) datasets. We find AvaTaR consistently outperforms state-of-the-art approaches across all seven tasks, exhibiting strong generalization ability when applied to novel cases and achieving an average relative improvement of 14% on the Hit@1 metric for the retrieval datasets and 13% for the QA datasets.

We present DreamWaltz, a novel framework for generating and animating complex 3D avatars given text guidance and parametric human body prior. While recent methods have shown encouraging results for text-to-3D generation of common objects, creating high-quality and animatable 3D avatars remains challenging. To create high-quality 3D avatars, DreamWaltz proposes 3D-consistent occlusion-aware Score Distillation Sampling (SDS) to optimize implicit neural representations with canonical poses. It provides view-aligned supervision via 3D-aware skeleton conditioning which enables complex avatar generation without artifacts and multiple faces. For animation, our method learns an animatable 3D avatar representation from abundant image priors of diffusion model conditioned on various poses, which could animate complex non-rigged avatars given arbitrary poses without retraining. Extensive evaluations demonstrate that DreamWaltz is an effective and robust approach for creating 3D avatars that can take on complex shapes and appearances as well as novel poses for animation. The proposed framework further enables the creation of complex scenes with diverse compositions, including avatar-avatar, avatar-object and avatar-scene interactions.

In this paper, we aim to create generalizable and controllable neural signed distance fields (SDFs) that represent clothed humans from monocular depth observations. Recent advances in deep learning, especially neural implicit representations, have enabled human shape reconstruction and controllable avatar generation from different sensor inputs. However, to generate realistic cloth deformations from novel input poses, watertight meshes or dense full-body scans are usually needed as inputs. Furthermore, due to the difficulty of effectively modeling pose-dependent cloth deformations for diverse body shapes and cloth types, existing approaches resort to per-subject/cloth-type optimization from scratch, which is computationally expensive. In contrast, we propose an approach that can quickly generate realistic clothed human avatars, represented as controllable neural SDFs, given only monocular depth images.

The Download: China's dying EV batteries, and why AI doomers are doubling down China figured out how to sell EVs. Now it has to bury their batteries. In the past decade, China has seen an EV boom, thanks in part to government support. Buying an electric car has gone from a novel decision to a routine one; by late 2025, nearly 60% of new cars sold were electric or plug-in hybrids. But as the batteries in China's first wave of EVs reach the end of their useful life, early owners are starting to retire their cars, and the country is now under pressure to figure out what to do with those aging components. The issue is putting strain on China's still-developing battery recycling industry and has given rise to a gray market that often cuts corners on safety and environmental standards.

Director James Cameron criticizes Trump administration's environmental rollbacks and warns humanity is going backwards in interview about "Avatar: Fire and Ash."