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cs.CV2025

Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing

Ling-Hao Chen, Shunlin Lu, Wenxun Dai +5

This research delves into the problem of interactive editing of human motion generation. Previous motion diffusion models lack explicit modeling of the word-level text-motion corre…

cs.CV2024

MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model

Wenxun Dai, Ling-Hao Chen, Jingbo Wang +3

This work introduces MotionLCM, extending controllable motion generation to a real-time level. Existing methods for spatial-temporal control in text-conditioned motion generation s…

cs.CV2024

ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model

Shunlin Lu, Jingbo Wang, Zeyu Lu +6

The scaling law has been validated in various domains, such as natural language processing (NLP) and massive computer vision tasks; however, its application to motion generation re…

cs.CV2024

MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning

Yuming Feng, Zhiyang Dou, Ling-Hao Chen +7

Modeling temporal characteristics and the non-stationary dynamics of body movement plays a significant role in predicting human future motions. However, it is challenging to captur…

cs.CV2024

MotionLLM: Understanding Human Behaviors from Human Motions and Videos

Ling-Hao Chen, Shunlin Lu, Ailing Zeng +4

This study delves into the realm of multi-modality (i.e., video and motion modalities) human behavior understanding by leveraging the powerful capabilities of Large Language Models…