5 papers
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang +4
In-context learning is a promising paradigm that utilizes in-context examples as prompts for the predictions of large language models. These prompts are crucial for achieving stron…
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…
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…
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…
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…