2 citations · 8 across the 7 of their papers we have counts for
7 papers · 1 filter
HOI-Diff: Text-Driven Synthesis of 3D Human-Object Interactions using Diffusion Models
Xiaogang Peng, Yiming Xie, Zizhao Wu +3
We address the problem of generating realistic 3D human-object interactions (HOIs) driven by textual prompts. To this end, we take a modular design and decompose the complex task i…
The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction
Xiaogang Peng, Xiao Zhou, Yikai Luo +3
3D multi-person motion prediction is a challenging task that involves modeling individual behaviors and interactions between people. Despite the emergence of approaches for this ta…
Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space
Xiaogang Peng, Hao Wen, Yikai Luo +4
In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multi…
Trajectory-Aware Body Interaction Transformer for Multi-Person Pose Forecasting
Xiaogang Peng, Siyuan Mao, Zizhao Wu
Multi-person pose forecasting remains a challenging problem, especially in modeling fine-grained human body interaction in complex crowd scenarios. Existing methods typically repre…
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding
Honggu Zhou, Xiaogang Peng, Jiawei Mao +2
Some self-supervised cross-modal learning approaches have recently demonstrated the potential of image signals for enhancing point cloud representation. However, it remains a quest…
More comprehensive facial inversion for more effective expression recognition
Jiawei Mao, Guangyi Zhao, Yuanqi Chang +3
Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acqu…
SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction
Xiaogang Peng, Yaodi Shen, Haoran Wang +3
Multi-person motion prediction remains a challenging problem, especially in the joint representation learning of individual motion and social interactions. Most prior methods only…