17 citations · 41 across the 9 of their papers we have counts for
9 papers
Multi-Modality Co-Learning for Efficient Skeleton-based Action Recognition
Jinfu Liu, Chen Chen, Mengyuan Liu
Skeleton-based action recognition has garnered significant attention due to the utilization of concise and resilient skeletons. Nevertheless, the absence of detailed body informati…
ClickDiff: Click to Induce Semantic Contact Map for Controllable Grasp Generation with Diffusion Models
Peiming Li, Ziyi Wang, Mengyuan Liu +2
Grasp generation aims to create complex hand-object interactions with a specified object. While traditional approaches for hand generation have primarily focused on visibility and…
SATO: Stable Text-to-Motion Framework
Wenshuo Chen, Hongru Xiao, Erhang Zhang +4
Is the Text to Motion model robust? Recent advancements in Text to Motion models primarily stem from more accurate predictions of specific actions. However, the text modality typic…
MLP: Motion Label Prior for Temporal Sentence Localization in Untrimmed 3D Human Motions
Sheng Yan, Mengyuan Liu, Yong Wang +3
In this paper, we address the unexplored question of temporal sentence localization in human motions (TSLM), aiming to locate a target moment from a 3D human motion that semantical…
Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition
Mengyuan Liu, Chen Chen, Songtao Wu +2
Recognizing interactive actions, including hand-to-hand interaction and human-to-human interaction, has attracted increasing attention for various applications in the field of vide…
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +1
The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction.Various styles of graph convolutions have been proposed, with eac…
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Xinshun Wang, Zhongbin Fang, Xia Li +2
In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting, the model can perceive tasks from prompts and accomplish them without…
A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation
Qitao Zhao, Ce Zheng, Mengyuan Liu +1
The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for…
Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning
Yang Liu, Chen Chen, Can Wang +2
Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods still hav…