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The official release of Tencent HunYuan 3D generative model version 2.5 marks a significant leap into the ultra-high-definition era of 3D generation technology. This upgrade not only achieves a qualitative leap in modeling precision but also provides users with a more efficient and convenient creative experience, further lowering the barrier to entry for 3D content creation.

Fudan University and Jieyue Xingchen, a leading domestic AI innovation company, recently announced the upcoming release of OmniSVG, an end-to-end multimodal SVG generation model. This news has quickly garnered widespread attention in the technology and design fields. According to AIbase, OmniSVG's core strength lies in its powerful generation capabilities, supporting vector graphic generation from simple icons to complex anime characters, providing a new intelligent solution for digital art creation. The launch of this model is poised to redefine the technical boundaries of vector graphic generation. Multimodal Generation: Flexible response.

Tencent HunYuan recently announced the launch of five new open-source 3D generative models. Built upon Hunyuan3D-2.0, these models boast faster generation speeds, richer details, and more realistic material expression. Simultaneously, Tencent's self-developed 3D AI creation engine has been upgraded, adding features such as multi-view input, intelligent model simplification, and full format compatibility. It is now fully open to C-end users. Among these new models, the Turbo series maintains high precision and quality while optimizing the geometric generation model...

Recently, the research team at Meta Reality Labs has jointly released an innovative generative model called 'Pippo', which can generate a dense turnaround video with a resolution of up to 1K from a randomly taken photo. This breakthrough technology marks another significant advancement in the fields of computer vision and image generation. The core of the Pippo model lies in the design of its multi-view diffusion transformer. Unlike traditional generative models, Pippo does not require any additional inputs, such as fitted parameters.

A research team from New York University, MIT, and Google has recently proposed an innovative framework aimed at addressing the bottleneck of inference time scaling in diffusion models. This groundbreaking research transcends the traditional method of simply increasing the denoising steps, opening up new avenues for improving generative model performance. The framework primarily unfolds from two dimensions: one is leveraging feedback from validators, and the second is implementing algorithms to discover better noise candidates. The research team builds on a pre-trained SiT-XL model with a resolution of 256×256, maintaining 250 fixed denoising steps.

Recently, Yingmou Technology announced the completion of a Series A financing round of several million dollars, attracting widespread attention in the industry. This financing was jointly led by Meituan Longzhu and ByteDance, with existing shareholders Sequoia China Seed Fund and Qiji Chuangtan also participating actively, while Guangsou Capital acted as the exclusive financial advisor. This infusion of funds will provide strong support for Yingmou Technology in the research and commercialization of 3D large models. Founded in 2020 by a group of young people with an average age of only 24, the team members include Wu Di and Zhang Qixuan.

Robot startup 1X Technologies has developed a new generative model that enhances the efficiency of training robotic systems in simulations. The company announced this model in a new blog post, addressing one of the significant challenges in robotics, which is learning the 'World Model (1X World Model)' that predicts how the world responds to the actions of robots. When robots start from the same sequence of initial images, this world model can envision multiple possibilities based on different action proposals.

Multimodal generative models are leading a new trend in artificial intelligence, focusing on the integration of visual and textual data to create multifunctional AI systems that perform various tasks, from image generation to understanding and reasoning across different data types. A key challenge is to enhance the capabilities of autoregressive (AR) models so they can generate high-detail images based on textual descriptions. While diffusion models excel in generating high-quality images, AR models lag behind in image quality, resolution flexibility, and multitasking capabilities. Researchers from Shanghai AI Lab and The Chinese University of Hong Kong have introduced Lum

Recently, Stability AI announced the release of a revolutionary video processing technology—Stable Video 4D. This technology can convert a single-view video into a new view video from 8 different angles, providing creators with unprecedented flexibility and creativity. Stable Video 4D is built on the foundation of the previously released Stable Video Diffusion model. Unlike converting images into videos, the new model can take video input and generate...

Tokyo-based startup Sakana AI has introduced two new image generation models — Evo-Ukiyoe and Evo-Nishikie. Image from Sakana AI official These models can be found on the Hugging Face platform, with the main purpose of generating Ukiyo-e style art through text or image prompts. As is known, Ukiyo-e is a traditional Japanese art form popular from the 17th to the 19th century, depicting themes such as historical scenes, natural landscapes, and sumo wrestlers. Sakana AI aims to use AI to bring thi

Apple Inc. recently unveiled the core technology behind its artificial intelligence platform, Apple Intelligence—a device-side model with approximately 3 billion parameters and a server-based language model. In a series of evaluations, Apple Intelligence outperformed open-source models including Phi-3, Gemma, Mistral, DBRX, as well as commercial models like GPT-3.5-Turbo and GPT-4-Turbo, often being rated higher than human evaluations.

Recently, scientists published a study on protein-ligand complex structure prediction in the journal 'Nature Machine Intelligence'. The new method, NeuralPLexer, predicts structures using deep generative models with only the protein sequence and ligand molecule graph as input. NeuralPLexer leverages a multiscale deep learning system to enhance prediction success rates and is expected to play a significant role in drug discovery. This research provides insights for exploring the field of protein-ligand complex structure prediction.

The global generative artificial intelligence market size is expected to grow from $10.5 billion in 2022 to $208.8 billion by 2032, with a compound annual growth rate (CAGR) of 35.1%. The North American market currently dominates, but significant growth is expected in the Asia-Pacific region, particularly in China, over the coming years. Generative AI technology has vast application prospects and is poised to fundamentally change the future of content production. Despite the enormous growth potential in the market, challenges such as high implementation costs and talent shortages remain in the technology application process.

UK startup Wayve released GAIA-1 in June 2023, a generative AI model designed for training data for autonomous vehicles. GAIA-1 uses text, images, videos, and action data to generate synthetic videos of various traffic scenarios, filling a data gap in autonomous driving training. The model now boasts 9 billion parameters and can efficiently generate high-resolution videos, making it a powerful tool for the training and validation of autonomous driving systems. Wayve is also developing Lingo-1, a combination of machine vision.