2 citations · 4 across the 7 of their papers we have counts for
7 papers
DeforHMR: Vision Transformer with Deformable Cross-Attention for 3D Human Mesh Recovery
Jaewoo Heo, George Hu, Zeyu Wang +1
Human Mesh Recovery (HMR) is an important yet challenging problem with applications across various domains including motion capture, augmented reality, and biomechanics. Accurately…
Motion Diffusion-Guided 3D Global HMR from a Dynamic Camera
Jaewoo Heo, Kuan-Chieh Wang, Karen Liu +1
Motion capture technologies have transformed numerous fields, from the film and gaming industries to sports science and healthcare, by providing a tool to capture and analyze human…
Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision
Orr Zohar, Xiaohan Wang, Yonatan Bitton +2
The performance of Large Vision Language Models (LVLMs) is dependent on the size and quality of their training datasets. Existing video instruction tuning datasets lack diversity a…
μ-Bench: A Vision-Language Benchmark for Microscopy Understanding
Alejandro Lozano, Jeffrey Nirschl, James Burgess +4
Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising…
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
Xiaohan Wang, Yuhui Zhang, Orr Zohar +1
Long-form video understanding represents a significant challenge within computer vision, demanding a model capable of reasoning over long multi-modal sequences. Motivated by the hu…
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
Yuhui Zhang, Elaine Sui, Serena Yeung-Levy
Building cross-modal applications is challenging due to limited paired multi-modal data. Recent works have shown that leveraging a pre-trained multi-modal contrastive representatio…