collaborators

7 papers

cs.CV2026

BVB: Benchmarking Agentic Video Understanding via Programmatic Reconstruction in Blender

Yolo Y. Tang, Daiki Shimada, Jiayue Meng +14

Multimodal agents can create complex videos in software such as Blender by writing code instead of using diffusion models. Yet video understanding benchmarks still evaluate models…

cs.MA2026

GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing

Zihao Lin, Haibo Wang, Zhiyang Xu +7

Music-grounded mashup video creation is a challenging form of video non-linear editing, where a system must compose a coherent timeline from large collections of source videos whil…

cs.CV2026

Suppression Is Not Forgetting: Residual Recoverability in Visual Concept Unlearning for VLMs

Zhangyun Tan, Zeliang Zhang, Jiani Liu +5

Vision-language models (VLMs) may need to forget visual concepts after deployment because of privacy, copyright, licensing, safety, or policy changes. Conventional machine unlearni…

cs.CV2026

TDMM-LM: Bridging Facial Understanding and Animation via Language Models

Luchuan Song, Pinxin Liu, Haiyang Liu +7

Text-guided human body animation has advanced rapidly, yet facial animation lags due to the scarcity of well-annotated, text-paired facial corpora. To close this gap, we leverage f…

cs.CV2026

Omni-Judge: Can Omni-LLMs Serve as Human-Aligned Judges for Text-Conditioned Audio-Video Generation?

Susan Liang, Chao Huang, Filippos Bellos +7

State-of-the-art text-to-video generation models such as Sora 2 and Veo 3 can now produce high-fidelity videos with synchronized audio directly from a textual prompt, marking a new…

cs.CV2025

Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination

Yolo Y. Tang, Daiki Shimada, Hang Hua +4

Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fi…

cs.AI2025

AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning

Zhenyu Pan, Yiting Zhang, Zhuo Liu +13

LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…