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AI technology company Sync Labs recently announced via Twitter the launch of its latest product, Lipsync-2, touted as the world's first zero-shot lip-sync model. It retains the speaker's unique style without requiring additional training or fine-tuning. This breakthrough technology boasts significant improvements in realism, expressiveness, control, quality, and speed, suitable for real-person videos, animation, and AI-generated content. Lipsync-2's innovative features are based on Sync Lab's...

Recently, developer scraed released LanPaint on GitHub, a zero-shot image inpainting tool. This tool aims to help users achieve high-quality image inpainting results on any Stable Diffusion (SD) model, including custom-trained models. LanPaint achieves this by iteratively prompting the model to 'think' before denoising, resulting in more seamless and accurate inpainting. A key feature of LanPaint is its zero-shot capability; users can start using it immediately without any training.

Recently, Oute AI released a novel text-to-speech synthesis method called OuteTTS-0.1-350M. This method utilizes pure language modeling without the need for external adapters or complex architectures, offering a simplified TTS approach. OuteTTS-0.1-350M is based on the LLaMa architecture, using WavTokenizer to directly generate audio tokens, making the process more efficient. The model features zero-shot voice cloning capability, requiring only a few seconds of reference audio.

In today's rapidly advancing technology, speech synthesis technology is also progressing, especially in the field of restoring lost voices. Recently, Google researchers introduced a new technology called 'Zero-shot Voice Transfer' which can be directly integrated with state-of-the-art Text-to-Speech (TTS) systems to help those who have lost their voices due to illness or accidents regain their 'voice memory'. The core of this technology is its 'zero-shot' capability, meaning that we do not need a large number of samples to achieve this.

小冰公司的最新发布标志着其AI数字员工产品的重大升级，新增“零样本”数字人技术（Zero-shot Xiaoice Neural Rendering，Zero-XNR）、超千亿大模型基座以及高能多媒体传输系统，显著提升实时交互质量和效果。Zero-XNR技术结合TTS语音模型，并配以高效聚类框架实现秒级高质量声音与形象复刻。大模型基座与Agent构建框架的强化集结了强大的职业交互功能，用于精准商业交互。透影音画传输系统的引入，则确保了超高清视频的传输流畅性与抗干扰性，增强了用户体验。在技术创新引领下，小冰旨在推动数字人技术的普及与产业化应用，经过微软背景的专业打磨，现已成为一家独享核心技术的中国AI公司。其技术框架涵盖了自然语言处理、语音识别、视觉交互及AI内容生成，已成功构建全球化的AI数字人产品体系，服务从金融到教育、智能汽车、智能地产等众多行业，支持数字工作者提供平稳、可靠并生产力充足的交互体验。

Image matching is a fundamental task in computer vision. Recently, matching models based on deep learning have gradually gained popularity. Xiamen University, Intel, and DJI have introduced GIM: Learning Generalizable Image Matcher from Internet Videos. GIM enables matching models to learn strong generalization capabilities from internet videos. The GIM framework is suitable for all matching models, including DKM, LoFTR, and SuperGlue. The authors have proposed the first ...