Keming Shen — accepted AI-conference papers

2026

FineXtrol: Controllable Motion Generation via Fine-Grained Text

Keming Shen, Bizhu Wu, Junliang Chen, Xiaoqin Wang, Linlin Shen

AAAI 2026technical

Recent works have sought to enhance the controllability and precision of text-driven motion generation. Some approaches leverage large language models (LLMs) to produce more detailed texts, while others incorporate global 3D coordinate sequences as additional control signals. However, the former oft

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2025

MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities

Bizhu Wu, Jinheng Xie, Keming Shen, Zhe Kong, Jianfeng Ren, Ruibin Bai +2

CVPR 2025poster

Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coarse-grained motion-text modeling, where text describes the overall semantics of an entire motion sequence in just a few…

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