2 citations · 3 across the 10 of their papers we have counts for
13 papers
MotionRFT: Unified Reinforcement Fine-Tuning for Text-to-Motion Generation
Xiaofeng Tan, Wanjiang Weng, Hongsong Wang +3
Text-to-motion generation has advanced with diffusion- and flow-based generative models, yet supervised pre-training remains insufficient to align models with high-level objectives…
Temporal Consistency-Aware Text-to-Motion Generation
Hongsong Wang, Wenjing Yan, Qiuxia Lai +1
Text-to-Motion (T2M) generation aims to synthesize realistic human motion sequences from natural language descriptions. While two-stage frameworks leveraging discrete motion repres…
Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees
Haodong Lei, Hongsong Wang, Xin Geng +2
Autoregressive (AR) image models achieve diffusion-level quality but suffer from sequential inference, requiring approximately 2,000 steps for a 576x576 image. Speculative decoding…
Foundation Model for Skeleton-Based Human Action Understanding
Hongsong Wang, Wanjiang Weng, Junbo Wang +4
Human action understanding serves as a foundational pillar in the field of intelligent motion perception. Skeletons serve as a modality- and device-agnostic representation for huma…
PAMD: Plausibility-Aware Motion Diffusion Model for Long Dance Generation
Hongsong Wang, Yin Zhu, Qiuxia Lai +3
Computational dance generation is crucial in many areas, such as art, human-computer interaction, virtual reality, and digital entertainment, particularly for generating coherent a…
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning
Shunxin Guo, Jiaqi Lv, Xin Geng
We introduce Ring-topology Decentralized Federated Learning (RDFL) for distributed model training, aiming to avoid the inherent risks of centralized failure in server-based FL. How…
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning
Shunxin Guo, Jiaqi Lv, Qiufeng Wang +1
Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAF…
STHFL: Spatio-Temporal Heterogeneous Federated Learning
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is a new framework that protects data privacy and allows multiple devices to cooperate in training machine learning models. Previous studies have proposed multip…
SoPo: Text-to-Motion Generation Using Semi-Online Preference Optimization
Xiaofeng Tan, Hongsong Wang, Xin Geng +1
Text-to-motion generation is essential for advancing the creative industry but often presents challenges in producing consistent, realistic motions. To address this, we focus on fi…
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection
Xiaofeng Tan, Hongsong Wang, Xin Geng +1
Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. However, these methods struggle with t…
Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
Shunxin Guo, Hongsong Wang, Shuxia Lin +2
Federated learning is an efficient framework designed to facilitate collaborative model training across multiple distributed devices while preserving user data privacy. A significa…
Controllable Dance Generation with Style-Guided Motion Diffusion
Hongsong Wang, Ying Zhu, Xin Geng +1
Dance plays an important role as an artistic form and expression in human culture, yet automatically generating dance sequences is a significant yet challenging endeavor. Existing…
Dynamic Heterogeneous Federated Learning with Multi-Level Prototypes
Shunxin Guo, Hongsong Wang, Xin Geng
Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing heterogeneous federated learning mainly focuses on skewing the label distributio…