CORTEXA
← Browse

Hao Feng

4 papers indexed

arxivcs.CV2026-07-09

SkelGen4D: Weakly-Supervised Skeleton-Based 4D Generation for Text-Driven Mesh Animation

Hao Feng, Zhi Zuo, Jia-Hui Pan, Ka-Hei Hui, Zhengzhe Liu, Dian Zhang, et al.

We study 4D generation to synthesize temporally coherent sequences of 3D geometry for animation and content creation. In contrast to existing SDS-based optimization methods and video-driven animation approaches, we adopt a skeleton-driven animation framework aligned with standard…

View free PDFSource page
arxivcs.CV2026-07-06

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

Gengluo Li, Xingyu Wan, Shangpin Peng, Weinong Wang, Hao Feng, Yongkun Du, et al.

We present HunyuanOCR-1.5, a lightweight end-to-end OCR-specialized vision-language model. HunyuanOCR unifies document parsing, text spotting, information extraction, text-image translation, and multi-image document understanding within a single end-to-end VLM. Building upon the…

View free PDFSource page
arxivcs.CVcs.CL2026-07-03

MORE: A Multilingual Document Parsing Benchmark and Evaluation

Long Xu, Binghong Wu, Tinghao Yu, Hao Feng, Zhenyu Huang, Haoqing Jiang, et al.

Multilingual documents encapsulate rich regional cultures, scientific discoveries, and historical records. Parsing this content into structured, machine-readable formats is critical for unlocking global knowledge. However, existing benchmarks predominantly focus on high-resource…

View free PDFSource page
arxivcs.CV2026-06-29

StrucTab: A Structured Optimization Framework for Table Parsing

Gengluo Li, Shangpin Peng, Chengquan Zhang, Binghong Wu, Hao Feng, Weinong Wang, et al.

Table parsing aims to convert table images into structured, machine-readable representations, a task requiring the joint perception of complex spatial layouts and textual content. While recent vision-language models (VLMs) enable end-to-end parsing, they typically rely on direct…

View free PDFSource page