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Blender-MCP (Model Context Protocol) has been officially open-sourced, enabling seamless integration of Anthropic's Claude AI with Blender. This breakthrough allows users to create complex 3D scenes using natural language prompts. According to AIbase, the tool allows users to generate sophisticated 3D models with text descriptions alone, such as a scene depicting a low-poly dragon guarding treasure, significantly lowering the technical barrier to entry for 3D modeling. Blender-MCP

Roblox recently released Cube, a 3D intelligent base model, marking a significant advancement for game developers who can now leverage the power of artificial intelligence in designing game assets. Roblox has always encouraged users to create their own games, items, and clothing, and now this process has become simpler and more efficient. Cube aims to provide developers with a powerful tool capable of generating various in-game experiences. Whether it's 3D objects, scene design, character modeling, or programming scripts, Cube...

Startup company Meshy has launched its AI-driven 3D modeling tool Meshy-4, achieving significant improvements in model geometry and workflow after 16 months of development, greatly enhancing the creative efficiency of designers and developers. Meshy-4 excels in the field of AI-generated 3D models, producing smoother surfaces and clearer geometric details, approaching professional use standards. Its innovative text-to-3D model generation process is divided into two stages: modeling and texturing, increasing user control over the final output and paving the way for future specialized features.

A collaborative effort between the University of Alberta, the University of Toronto, and Huawei's Noah's Ark Lab has led to the development of a new method called TexGen, aimed at solving the challenge of generating realistic textures in the field of 3D modeling. This technology generates corresponding 3D textures from text descriptions, significantly outperforming existing techniques. The core involves a multi-view sampling and re-sampling framework combined with a pre-trained text-to-image diffusion model. This method effectively reduces discrepancies between views by maintaining a texture map with RGB parameters and updating it after each sampling, achieving view-consistent texture generation. An attention guidance mechanism has been introduced.

Researchers have developed the 3D-GPT system, powered by large language models (LLM), which can generate complex 3D models and scenes based on brief text descriptions. The system breaks down 3D modeling tasks into manageable components through a multi-agent system, including task scheduling agents, conceptualization agents, and modeling agents. The key role of the task scheduling agent is to select the appropriate program generation functions based on user instructions, improving the efficiency of task coordination. 3D-GPT supports continuous instruction input, allowing users to edit and modify generated scenes, including object control.