57 citations · 151 across the 17 of their papers we have counts for
18 papers
FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving
Tengju Ye, Wei Jing, Chunyong Hu +11
Building a multi-modality multi-task neural network toward accurate and robust performance is a de-facto standard in perception task of autonomous driving. However, leveraging such…
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
Muzhi Zhu, Hengtao Li, Hao Chen +5
Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel object…
Joint-Relation Transformer for Multi-Person Motion Prediction
Qingyao Xu, Weibo Mao, Jingze Gong +5
Multi-person motion prediction is a challenging problem due to the dependency of motion on both individual past movements and interactions with other people. Transformer-based meth…
CTVIS: Consistent Training for Online Video Instance Segmentation
Kaining Ying, Qing Zhong, Weian Mao +7
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is direc…
VisFusion: Visibility-aware Online 3D Scene Reconstruction from Videos
Huiyu Gao, Wei Mao, Miaomiao Liu
We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric feature…
Leapfrog Diffusion Model for Stochastic Trajectory Prediction
Weibo Mao, Chenxin Xu, Qi Zhu +2
To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models…
Interacting Hand-Object Pose Estimation via Dense Mutual Attention
Rong Wang, Wei Mao, Hongdong Li
3D hand-object pose estimation is the key to the success of many computer vision applications. The main focus of this task is to effectively model the interaction between the hand…
Contact-aware Human Motion Forecasting
Wei Mao, Miaomiao Liu, Richard Hartley +1
In this paper, we tackle the task of scene-aware 3D human motion forecasting, which consists of predicting future human poses given a 3D scene and a past human motion. A key challe…
Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann
We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short mot…
Remember Intentions: Retrospective-Memory-based Trajectory Prediction
Chenxin Xu, Weibo Mao, Wenjun Zhang +1
To realize trajectory prediction, most previous methods adopt the parameter-based approach, which encodes all the seen past-future instance pairs into model parameters. However, in…
Poseur: Direct Human Pose Regression with Transformers
Weian Mao, Yongtao Ge, Chunhua Shen +4
We propose a direct, regression-based approach to 2D human pose estimation from single images. We formulate the problem as a sequence prediction task, which we solve using a Transf…
Generating Smooth Pose Sequences for Diverse Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann
Recent progress in stochastic motion prediction, i.e., predicting multiple possible future human motions given a single past pose sequence, has led to producing truly diverse futur…
Multi-level Motion Attention for Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann +1
Human motion prediction aims to forecast future human poses given a historical motion. Whether based on recurrent or feed-forward neural networks, existing learning based methods f…
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
Weian Mao, Zhi Tian, Xinlong Wang +1
We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose. Different from existing methods, which often requi…
TFPose: Direct Human Pose Estimation with Transformers
Weian Mao, Yongtao Ge, Chunhua Shen +3
We propose a human pose estimation framework that solves the task in the regression-based fashion. Unlike previous regression-based methods, which often fall behind those state-of-…
History Repeats Itself: Human Motion Prediction via Motion Attention
Wei Mao, Miaomiao Liu, Mathieu Salzmann
Human motion prediction aims to forecast future human poses given a past motion. Whether based on recurrent or feed-forward neural networks, existing methods fail to model the obse…
Cost Volume Pyramid Based Depth Inference for Multi-View Stereo
Jiayu Yang, Wei Mao, Jose M. Alvarez +1
We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of c…
Learning Trajectory Dependencies for Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann +1
Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced b…