Embodied3DBench creates a new evaluation benchmark for low-level embodied spatial intelligence in VLMs, evaluates 13 models showing gaps in interaction perception, and supplies a large synthetic training set that yields measurable gains.
JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
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.
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Formalizes Fashion Detail Generation task, releases FDBench benchmark with 40K+ pairs, and proposes CFAD distillation method plus RL consistency reward that outperforms open-source baselines.
Empirical analysis of data, guidance, and task mixture in few-step distillation of Qwen-Image-2.0 produces the Qwen-Image-Flash model with improved performance in unified generation and editing tasks.
citing papers explorer
-
Embodied3DBench: Benchmarking Low-Level Embodied Spatial Intelligence of Vision Language Models
Embodied3DBench creates a new evaluation benchmark for low-level embodied spatial intelligence in VLMs, evaluates 13 models showing gaps in interaction perception, and supplies a large synthetic training set that yields measurable gains.
-
DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation
Formalizes Fashion Detail Generation task, releases FDBench benchmark with 40K+ pairs, and proposes CFAD distillation method plus RL consistency reward that outperforms open-source baselines.
-
Qwen-Image-Flash: Beyond Objective Design
Empirical analysis of data, guidance, and task mixture in few-step distillation of Qwen-Image-2.0 produces the Qwen-Image-Flash model with improved performance in unified generation and editing tasks.