5 papers
Deterministic Decomposition of Stochastic Generative Dynamics
Xingyu Song, Yuan Mei, Naoya Takeishi
Modern generative models can be understood as probability transport from a simple base distribution to a target data distribution. Deterministic transport models offer tractable ve…
M: Reframing Training Measures for Discretized Physical Simulations
Yuan Mei, Xingyu Song, Xiaowen Song +1
Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to uneven supervision, biasing…
Quater-GCN: Enhancing 3D Human Pose Estimation with Orientation and Semi-supervised Training
Xingyu Song, Zhan Li, Shi Chen +1
3D human pose estimation is a vital task in computer vision, involving the prediction of human joint positions from images or videos to reconstruct a skeleton of a human in three-d…
An Animation-based Augmentation Approach for Action Recognition from Discontinuous Video
Xingyu Song, Zhan Li, Shi Chen +2
Action recognition, an essential component of computer vision, plays a pivotal role in multiple applications. Despite significant improvements brought by Convolutional Neural Netwo…
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Xingyu Song, Zhan Li, Shi Chen +1
Current datasets for action recognition tasks face limitations stemming from traditional collection and generation methods, including the constrained range of action classes, absen…