22 citations · 22 across the 8 of their papers we have counts for
9 papers
Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning
Yijun Yang, Shenghe Zheng, Wenbo Li +8
Despite the remarkable prowess of Vision-Language Models (VLMs) in general multimodal tasks, they remain fundamentally ``flat'' when reasoning about the physical world. We argue th…
ZimaBlue: Evolving Generalizable World Action Models through Scalable Video Pre-training
Xionghao Wu, Yijun Yang, Shiyang Zhou +17
Robotic manipulation faces a fundamental scaling challenge: robust generalization demands broad physical experience, yet action-labeled robot trajectories are expensive to collect…
SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks
Hongcheng Gao, Hailong Qu, Jingyi Tang +18
Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predomin…
JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
Lin Song, Wenbo Li, Guoqing Ma +16
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 spatia…
Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence
Yanbing Zhang, Bo Wang, Jianhui Liu +9
Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static obse…
TextLDM: Language Modeling with Continuous Latent Diffusion
Jiaxiu Jiang, Jingjing Ren, Wenbo Li +10
Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next step toward a single architect…
MC#: Mixture Compressor for Mixture-of-Experts Large Models
Wei Huang, Yue Liao, Yukang Chen +6
Mixture-of-Experts (MoE) effectively scales large language models (LLMs) and vision-language models (VLMs) by increasing capacity through sparse activation. However, preloading all…
TEXGen: a Generative Diffusion Model for Mesh Textures
Xin Yu, Ze Yuan, Yuan-Chen Guo +6
While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets…
Mixture Compressor for Mixture-of-Experts LLMs Gains More
Wei Huang, Yue Liao, Jianhui Liu +6
Mixture-of-Experts large language models (MoE-LLMs) marks a significant step forward of language models, however, they encounter two critical challenges in practice: 1) expert para…