most citedAppAgent: Multimodal Agents as Smartphone Users

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cs.CV202611 cited

AppAgent: Multimodal Agents as Smartphone Users

Chi Zhang, Zhao Yang, Jiaxuan Liu +6

Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based mult…

cs.CV2025

DreamFrame: Enhancing Video Understanding via Automatically Generated QA and Style-Consistent Keyframes

Zhende Song, Chenchen Wang, Jiamu Sheng +4

Recent large vision-language models (LVLMs) for video understanding are primarily fine-tuned with various videos scraped from online platforms. Existing datasets, such as ActivityN…

cs.CV2025

Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation

Mu Hu, Wei Yin, Chi Zhang +7

We introduce Metric3D v2, a geometric foundation model for zero-shot metric depth and surface normal estimation from a single image, which is crucial for metric 3D recovery. While…

cs.CV2024

MeshXL: Neural Coordinate Field for Generative 3D Foundation Models

Sijin Chen, Xin Chen, Anqi Pang +11

The polygon mesh representation of 3D data exhibits great flexibility, fast rendering speed, and storage efficiency, which is widely preferred in various applications. However, giv…

cs.CV2024

MotionChain: Conversational Motion Controllers via Multimodal Prompts

Biao Jiang, Xin Chen, Chi Zhang +4

Recent advancements in language models have demonstrated their adeptness in conducting multi-turn dialogues and retaining conversational context. However, this proficiency remains…