3D 生成、理解、编辑，一个模型全包了
以前做 3D 内容，生成、看懂、修改是三个分开的模型，各干各的，改个东西还得来回倒腾。腾讯这个新模型把三件事塞进同一个架构：你给它一句话，它能生成一个 3D 物体；你给它一个已有的 3D 模型加一句「把椅子腿换成金属的」，它能在不动其他部分的前提下改好；它还能理解一个 3D 场景里有什么、各部分什么关系。关键在训练数据：他们造了 8700 万条 3D 多模态样本，其中 1200 万条是编辑数据，专门教模型「改哪里、别动哪里」。测试里，它在生成和编辑上都达到目前最好水平。这不是你明天就能上手用的工具，但它意味着「3D 内容创作」正在从「会生成」走向「能听懂人话、按需修改」，离普通人用自然语言做 3D 更近了一步。
📄 原文摘要(英文)
Recent advances in image generation have demonstrated the potential of unified multimodal models that integrate understanding, generation, and editing. However, unified 3D modeling remains constrained by scarce multimodal data, particularly the lack of large-scale and geometrically consistent editing data. To address this limitation, we propose Hunyuan3D-Buffalo 1.0, a unified framework supporting 3D understanding, text-to-3D generation, instruction-guided 3D editing, and text-grounded part generation within a single architecture. To enable scalable training, we construct an 87M-scale 3D multimodal corpus, comprising 25M understanding samples, 50M text-to-3D pairs, and 12M editing pairs generated using Nano3D-v2. Architecturally, the framework combines Hunyuan3D-VLM for semantic, structural, and spatial understanding with Hunyuan3D DiT for high-fidelity 3D synthesis. The VLM provides multimodal semantic conditions for generation, while editing and part generation additionally condition the diffusion process on the source object representation to preserve its overall structure and unedited regions. Extensive experiments show that Hunyuan3D-Buffalo 1.0 achieves state-of-the-art or leading performance on text-to-3D generation and 3D editing benchmarks, while exhibiting strong understanding and part-generation capabilities. Our analysis further shows that both generation and understanding improve editing, demonstrating the effectiveness of unified 3D multimodal training. Project Page: https://tencent-hunyuan.github.io/Hunyuan3D-Buffalo1.0/