3D 生成、理解、编辑，一个模型全包了
以前做 3D 内容，生成、看懂、修改是三个分开的工具，你得来回倒腾。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 3D 模型；你给它一个模型，它能告诉你这是什么；你指着模型说『把轮子换成履带』，它只改该改的地方，其余原样保留。为了训练它，团队造了 8700 万条 3D 数据，其中 1200 万条是编辑指令对。它不是你明天就能上手用的工具，但这是 3D 内容生产从『各干各的』走向『一个模型通吃』的信号。
📄 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/