7 papers · 1 filter
QuantiPhy: A Quantitative Benchmark Evaluating Physical Reasoning Abilities of Vision-Language Models
Li Puyin, Tiange Xiang, Ella Mao +5
Understanding the physical world is essential for generalist AI agents. However, it remains unclear whether state-of-the-art vision perception models (e.g., large VLMs) can reason…
Repurposing 2D Diffusion Models for 3D Shape Completion
Yao He, Youngjoong Kwon, Tiange Xiang +2
We present a framework that adapts 2D diffusion models for 3D shape completion from incomplete point clouds. While text-to-image diffusion models have achieved remarkable success w…
A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding
Christina Liu, Alan Q. Wang, Joy Hsu +2
Recent tool-use frameworks powered by vision-language models (VLMs) improve image understanding by grounding model predictions with specialized tools. Broadly, these frameworks lev…
ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body
Juze Zhang, Changan Chen, Xin Chen +5
Human communication is inherently multimodal and social: words, prosody, and body language jointly carry intent. Yet most prior systems model human behavior as a translation task c…
T*: Re-thinking Temporal Search for Long-Form Video Understanding
Jinhui Ye, Zihan Wang, Haosen Sun +9
Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding…
Towards Fine-Grained Video Question Answering
Wei Dai, Alan Luo, Zane Durante +5
In the rapidly evolving domain of video understanding, Video Question Answering (VideoQA) remains a focal point. However, existing datasets exhibit gaps in temporal and spatial gra…