2 citations · 2 across the 26 of their papers we have counts for
14 papers · 1 filter
ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing
Xinghao Chen, Xiangbo Gao, Jiongze Yu +2
Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits that must preserve the original s…
AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation
Keshu Wu, Hao Zhang, Rui Gan +4
Air-ground transportation research increasingly relies on co-simulation, yet constructing scenarios remains labor-intensive and difficult to validate. More importantly, a generated…
Visko Orbis 1.0: A Live Model for Real-Time Interactive Long Video Generation
Xiangbo Gao, Siyuan Yang, Ping He +12
We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generation, and the update becomes visib…
Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
Xiangbo Gao, Xiukun Huang, Boyu Lu +5
Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural l…
Agentic Chain-of-Thought Steering for Efficient and Controllable LLM Reasoning
Yu Xia, Zhouhang Xie, Xin Xu +4
Large language models improve final-answer accuracy through extended chain-of-thought reasoning, but often spend tokens inefficiently and offer little inference-time control. Exist…
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Fangzhou Lin, Peiran Li, Lingyu Xu +12
Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture th…
Delta Forcing: Trust Region Steering for Interactive Autoregressive Video Generation
Yuheng Wu, Xiangbo Gao, Tianhao Chen +4
Interactive real-time autoregressive video generation is essential for applications such as content creation and world modeling, where visual content must adapt to dynamically evol…
VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
Xiangbo Gao, Sicong Jiang, Bangya Liu +12
As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional…
Physics-Aware Video Instance Removal Benchmark
Zirui Li, Xinghao Chen, Lingyu Jiang +5
Video Instance Removal (VIR) requires removing target objects while maintaining background integrity and physical consistency, such as specular reflections and illumination interac…
The Pulse of Motion: Measuring Physical Frame Rate from Visual Dynamics
Xiangbo Gao, Mingyang Wu, Siyuan Yang +4
While recent generative video models have achieved remarkable visual realism and are being explored as world models, true physical simulation requires mastering both space and time…
PISCO: Precise Video Instance Insertion with Sparse Control
Xiangbo Gao, Renjie Li, Xinghao Chen +4
The landscape of AI video generation is undergoing a pivotal shift: moving beyond general generation - which relies on exhaustive prompt-engineering and "cherry-picking" - towards…
SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation
Jiongze Yu, Xiangbo Gao, Pooja Verlani +4
Video Super-Resolution (VSR) aims to restore high-quality video frames from low-resolution (LR) estimates, yet most existing VSR approaches behave like black boxes at inference tim…
Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World
Joonkyung Kim, Wenxi Chen, Davood Soleymanzadeh +9
The integration of foundation models (FMs) into robotics has accelerated real-world deployment, while introducing new safety challenges arising from open-ended semantic reasoning a…
Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
Fangzhou Lin, Qianwen Ge, Lingyu Xu +7
AI systems increasingly produce fluent, correct, end-to-end outcomes. Over time, this erodes users' ability to explain, verify, or intervene. We define this divergence as the Capab…