5 papers
MotionMERGE: A Multi-granular Framework for Human Motion Editing, Reasoning, Generation, and Explanation
Bizhu Wu, Jinheng Xie, Wenting Chen +5
Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and nuanced control over body pa…
ReactMotion: Generating Reactive Listener Motions from Speaker Utterance
Cheng Luo, Bizhu Wu, Bing Li +5
In this paper, we introduce a new task, Reactive Listener Motion Generation from Speaker Utterance, which aims to generate naturalistic listener body motions that appropriately res…
FineXtrol: Controllable Motion Generation via Fine-Grained Text
Keming Shen, Bizhu Wu, Junliang Chen +2
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 detail…
FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing
Bizhu Wu, Jinheng Xie, Meidan Ding +5
Generating realistic human motions from textual descriptions has undergone significant advancements. However, existing methods often overlook specific body part movements and their…
MG-MotionLLM: A Unified Framework for Motion Comprehension and Generation across Multiple Granularities
Bizhu Wu, Jinheng Xie, Keming Shen +5
Recent motion-aware large language models have demonstrated promising potential in unifying motion comprehension and generation. However, existing approaches primarily focus on coa…