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cs.CV2026

Physics Question Scene Graph: Fine-grained Evaluation of Physical Plausibility in Text-to-Video Generation

Atin Pothiraj, Jaemin Cho, Yue Zhang +2

Video generation models are increasingly capable of producing realistic videos, but they still struggle to generate videos that follow basic physical laws. Compounding this is a la…

cs.CV2026

PhyMotion: Structured 3D Motion Reward for Physics-Grounded Human Video Generation

Yidong Huang, Zun Wang, Han Lin +6

Generating realistic human motion is a central yet unsolved challenge in video generation. While reinforcement learning (RL)-based post-training has driven recent gains in general…

cs.CV2026

LAGO: Language-Guided Adaptive Object-Region Focus for Zero-Shot Visual-Text Alignment

Junyi Hu, Qiji Zhou, Lei Zhang +1

Zero-shot visual recognition often depends on localized parts, attributes, and textures that are diluted in global image representations. Existing training-free localized methods a…

cs.CV2026

EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

Ziyang Wang, Yue Zhang, Shoubin Yu +6

Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire day or more of continuous visual experienc…

cs.CV2026

3DrawAgent: Teaching LLM to Draw in 3D with Early Contrastive Experience

Hongcan Xiao, Xinyue Xiao, Yilin Wang +2

Sketching in 3D space enables expressive reasoning about shape, structure, and spatial relationships, yet generating 3D sketches through natural language remains a major challenge.…

cs.CV2026

Gym-V: A Unified Vision Environment System for Agentic Vision Research

Fanqing Meng, Lingxiao Du, Jiawei Gu +9

As agentic systems increasingly rely on reinforcement learning from verifiable rewards, standardized ``gym'' infrastructure has become essential for rapid iteration, reproducibilit…

cs.CV2026

A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding

Yue Zhang, Liqiang Jing, Jia Li +4

Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos remains limited. Existing approa…

cs.CV2026

V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

Han Lin, Xichen Pan, Zun Wang +4

Pixel-space diffusion has recently re-emerged as a strong alternative to latent diffusion, enabling high-quality generation without pretrained autoencoders. However, standard pixel…

cs.CV2026

VisionCoach: Reinforcing Grounded Video Reasoning via Visual-Perception Prompting

Daeun Lee, Shoubin Yu, Yue Zhang +1

Video reasoning requires models to locate and track question-relevant evidence across frames. While reinforcement learning (RL) with verifiable rewards improves accuracy, it still…

cs.CV2026

AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories

Zun Wang, Han Lin, Jaehong Yoon +3

Maintaining spatial world consistency over long horizons remains a central challenge for camera-controllable video generation. Existing memory-based approaches often condition gene…

cs.CV2026

When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning

Shoubin Yu, Yue Zhang, Zun Wang +4

Despite rapid progress in MLLMs, visual spatial reasoning remains unreliable when correct answers depend on how a scene would appear under unseen or alternative viewpoints. Recent…

cs.CV2026

Hierarchy-Aware Multimodal Unlearning for Medical AI

Fengli Wu, Vaidehi Patil, Jaehong Yoon +2

Pretrained Multimodal Large Language Models (MLLMs) are increasingly used in sensitive domains such as medical AI, where privacy regulations like HIPAA and GDPR require specific re…

cs.CV2025

Prune-Then-Plan: Step-Level Calibration for Stable Frontier Exploration in Embodied Question Answering

Noah Frahm, Prakrut Patel, Yue Zhang +3

Large vision-language models (VLMs) have improved embodied question answering (EQA) agents by providing strong semantic priors for open-vocabulary reasoning. However, when used dir…

cs.CV2025

Planning with Sketch-Guided Verification for Physics-Aware Video Generation

Yidong Huang, Zun Wang, Han Lin +5

Recent video generation approaches increasingly rely on planning intermediate control signals such as object trajectories to improve temporal coherence and motion fidelity. However…

cs.CV2025

Error-Driven Scene Editing for 3D Grounding in Large Language Models

Yue Zhang, Zun Wang, Han Lin +5

Despite recent progress in 3D-LLMs, they remain limited in accurately grounding language to visual and spatial elements in 3D environments. This limitation stems in part from train…

cs.CV2025

MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation

Shoubin Yu, Yue Zhang, Ziyang Wang +2

Combining pre-trained expert models offers substantial potential for scalable multimodal reasoning, but building a unified framework remains challenging due to the increasing diver…

cs.CV2025

EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance

Zun Wang, Jaemin Cho, Jialu Li +4

Recent approaches for video generation with camera control often create anchor videos (i.e., rendered videos that approximate desired camera motions) to guide diffusion models as a…