喚醒統一多模態理解與生成中的空間智能

摘要

我們推出JoyAI-Image——一個面向視覺理解、文字生成圖像及指令引導圖像編輯的統一多模態基礎模型。該模型通過空間增強的多元大型語言模型（MLLM）與多元擴散轉換器（MMDiT）的耦合架構，讓感知與生成能力透過共享的多模態介面相互協作。圍繞此架構，我們構建了可擴展的訓練方案，融合了統一指令微調、長文本渲染監督、空間錨定數據以及通用與空間編輯信號。此設計使模型具備廣泛的多模態能力，同時強化了幾何感知推理與可控視覺合成。在理解、生成、長文本渲染和編輯等多項基準測試中，JoyAI-Image均實現了最先進或極具競爭力的性能。更重要的是，增強理解、可控空間編輯與新視角輔助推理之間形成的雙向循環，使模型能突破通用視覺能力，邁向更強大的空間智能。這些成果為統一視覺模型在視覺-語言-行動系統及世界模型等下游應用中開闢了可行路徑。

English

We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples a spatially enhanced Multimodal Large Language Model (MLLM) with a Multimodal Diffusion Transformer (MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combines unified instruction tuning, long-text rendering supervision, spatially grounded data, and both general and spatial editing signals. This design gives the model broad multimodal capability while strengthening geometry-aware reasoning and controllable visual synthesis. Experiments across understanding, generation, long-text rendering, and editing benchmarks show that JoyAI-Image achieves state-of-the-art or highly competitive performance. More importantly, the bidirectional loop between enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward stronger spatial intelligence. These results suggest a promising path for unified visual models in downstream applications such as vision-language-action systems and world models.

喚醒統一多模態理解與生成中的空間智能

Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

摘要

Support