most citedGenerative Reasoning Recommendation via LLMs

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cs.CV2026

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…

cs.CV2026

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…

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…