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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…
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…