19 citations · 34 across the 11 of their papers we have counts for
11 papers
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…
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…
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…
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…
One-Shot Learning as Instruction Data Prospector for Large Language Models
Yunshui Li, Binyuan Hui, Xiaobo Xia +9
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may comp…
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang +5
Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such…
HumanTOMATO: Text-aligned Whole-body Motion Generation
Shunlin Lu, Ling-Hao Chen, Ailing Zeng +4
This work targets a novel text-driven whole-body motion generation task, which takes a given textual description as input and aims at generating high-quality, diverse, and coherent…
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…
HumanMAC: Masked Motion Completion for Human Motion Prediction
Ling-Hao Chen, Jiawei Zhang, Yewen Li +3
Human motion prediction is a classical problem in computer vision and computer graphics, which has a wide range of practical applications. Previous effects achieve great empirical…
DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction
He Li, Shiyu Zhang, Xuejiao Li +6
Detectors with high coverage have direct and far-reaching benefits for road users in route planning and avoiding traffic congestion, but utilizing these data presents unique challe…