Christopher Tensmeyer — accepted AI-conference papers

2026

AnyDoc: Enhancing Document Generation via Large-Scale HTML/CSS Data Synthesis and Height-Aware Reinforcement Optimization

Jiawei Lin, Wanrong Zhu, Vlad I Morariu, Christopher Tensmeyer

CVPR 2026

Document generation has gained growing attention in the field of AI-driven content creation. In this work, we push its boundaries by introducing AnyDoc, a framework capable of handling multiple generation tasks across a wide spectrum of document categories, all represented in a unified HTML/CSS form

Cited by 0 Source ↗Scholar ↗

2023

Learning the Visualness of Text Using Large Vision-Language Models

Gaurav Verma, Ryan A. Rossi, Christopher Tensmeyer, Jiuxiang Gu, Ani Nenkova

EMNLP 2023long main

Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. This is particularly challenging with long-form text as text-to-im…

Cited by 0 Source ↗Scholar ↗