Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
Summary
The paper introduces JoyAI-Image, a unified multimodal foundation model that integrates a spatially enhanced MLLM with MMDiT to achieve state-of-the-art performance in visual understanding, text-to-image generation, and instruction-guided editing.
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Paper page - Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
Source: https://huggingface.co/papers/2605.04128 Authors:
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Abstract
JoyAI-Image integrates a spatially enhanced MLLM with MMDiT to achieve unified visual understanding, text-to-image generation, and instruction-guided image editing with enhanced spatial intelligence.
We present JoyAI-Image, a unified multimodal foundation model for visual understanding, text-to-image generation, and instruction-guided image editing. JoyAI-Image couples aspatially enhancedMultimodal Large Language Model(MLLM) with aMultimodal Diffusion Transformer(MMDiT), allowing perception and generation to interact through a shared multimodal interface. Around this architecture, we build a scalable training recipe that combinesunified 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 andcontrollable 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, thebidirectional loopbetween enhanced understanding, controllable spatial editing, and novel-view-assisted reasoning enables the model to move beyond general visual competence toward strongerspatial intelligence. These results suggest a promising path for unified visual models in downstream applications such asvision-language-action systemsandworld models.
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