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20232026
most citedEAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration

15 citations · 28 across the 21 of their papers we have counts for

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20 papers · 1 filter

cs.CV2026

Chat-Scene++: Exploiting Context-Rich Object Identification for 3D LLM

Haifeng Huang, Yilun Chen, Zehan Wang +2

Recent advancements in multi-modal large language models (MLLMs) have shown strong potential for 3D scene understanding. However, existing methods struggle with fine-grained object…

cs.CV2026

ImVideoEdit: Image-learning Video Editing via 2D Spatial Difference Attention Blocks

Jiayang Xu, Fan Zhuo, Majun Zhang +6

Current video editing models often rely on expensive paired video data, which limits their practical scalability. In essence, most video editing tasks can be formulated as a decoup…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…