1 citations · 1 across the 9 of their papers we have counts for
10 papers
Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation
Ruofan Hu, Shengyang Xu, Minjie Hong +5
Real-world knowledge resides in multimodal documents, necessitating retrieval-augmented generation (RAG) for accurate question answering. However, existing multimodal RAG models ar…
HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Grounding
Jimin Xu, Tianbao Wang, Tao Jin +1
Recent text-to-CAD approaches have shown promising results by leveraging large language models, but they often struggle with maintaining structural consistency in complex designs a…
SpatialReward: Verifiable Spatial Reward Modeling for Fine-Grained Spatial Consistency in Text-to-Image Generation
Sashuai Zhou, Qiang Zhou, Junpeng Ma +9
Recent advances in text-to-image (T2I) generation via reinforcement learning (RL) have benefited from reward models that assess semantic alignment and visual quality. However, most…
Orient Anything V2: Unifying Orientation and Rotation Understanding
Zehan Wang, Ziang Zhang, Jiayang Xu +5
This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orie…
Generative Reasoning Recommendation via LLMs
Minjie Hong, Zetong Zhou, Zirun Guo +5
Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning rec…
DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Ziang Zhang, Zehan Wang, Guanghao Zhang +5
Reasoning about dynamic spatial relationships is essential, as both observers and objects often move simultaneously. Although vision-language models (VLMs) and visual expertise mod…
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization
Minjie Hong, Zirun Guo, Yan Xia +4
Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data, but they often struggle with complex reasoning. While Reinforcement learning (RL) can boost reaso…
GenSpace: Benchmarking Spatially-Aware Image Generation
Zehan Wang, Jiayang Xu, Ziang Zhang +4
Humans can intuitively compose and arrange scenes in the 3D space for photography. However, can advanced AI image generators plan scenes with similar 3D spatial awareness when crea…
Depth Anything with Any Prior
Zehan Wang, Siyu Chen, Lihe Yang +4
This work presents Prior Depth Anything, a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in…
RoboGround: Robotic Manipulation with Grounded Vision-Language Priors
Haifeng Huang, Xinyi Chen, Yilun Chen +6
Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding m…