5 papers
Unifying Adversarially Robust Model Experts in Vision-Language Models
Nguyen Duc Thai, Junhao Dong, Sua Qi Rong +2
Vision-language models (VLMs), such as CLIP, are vulnerable to adversarial attacks, posing a serious problem for real-life applications and deployment. Adversarial fine-tuning emer…
A Plug-and-Play Multi-Criteria Guidance for Diverse In-Betweening Human Motion Generation
Hua Yu, Jiao Liu, Xu Gui +3
In-betweening human motion generation aims to synthesize intermediate motions that transition between user-specified keyframes. In addition to maintaining smooth transitions, a cru…
A Spatio-temporal Continuous Network for Stochastic 3D Human Motion Prediction
Hua Yu, Yaqing Hou, Xu Gui +3
Stochastic Human Motion Prediction (HMP) has received increasing attention due to its wide applications. Despite the rapid progress in generative fields, existing methods often fac…
Deterministic-to-Stochastic Diverse Latent Feature Mapping for Human Motion Synthesis
Yu Hua, Weiming Liu, Gui Xu +3
Human motion synthesis aims to generate plausible human motion sequences, which has raised widespread attention in computer animation. Recent score-based generative models (SGMs) h…
DivDiff: A Conditional Diffusion Model for Diverse Human Motion Prediction
Hua Yu, Yaqing Hou, Wenbin Pei +1
Diverse human motion prediction (HMP) aims to predict multiple plausible future motions given an observed human motion sequence. It is a challenging task due to the diversity of po…