10 citations · 20 across the 11 of their papers we have counts for
12 papers
InteractGesture: Progressive Chunk Guidance for Continuous Streaming Co-Speech Gesture Control
Ekkasit Pinyoanuntapong, Ajinkya Deogade, Paul Streli +5
Co-speech gesture generation has made significant progress toward realistic full-body motion from speaker audio, yet existing models lack fine-grained spatial controllability of in…
KHMP: Frequency-Domain Kalman Refinement for High-Fidelity Human Motion Prediction
Wenhan Wu, Zhishuai Guo, Chen Chen +4
Stochastic human motion prediction aims to generate diverse, plausible futures from observed sequences. Despite advances in generative modeling, existing methods often produce pred…
BAMM: Bidirectional Autoregressive Motion Model
Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang +3
Generating human motion from text has been dominated by denoising motion models either through diffusion or generative masking process. However, these models face great limitations…
MMM: Generative Masked Motion Model
Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee +1
Recent advances in text-to-motion generation using diffusion and autoregressive models have shown promising results. However, these models often suffer from a trade-off between rea…
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
Ekkasit Pinyoanuntapong, Ayman Ali, Kalvik Jakkala +5
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy prote…
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
Ekkasit Pinyoanuntapong, Ayman Ali, Pu Wang +2
Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton…
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
Guangyu Sun, Umar Khalid, Matias Mendieta +2
Federated learning (FL) has emerged as a promising paradigm for enabling the collaborative training of models without centralized access to the raw data on local devices. In the ty…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
Ce Zheng, Matias Mendieta, Pu Wang +2
Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model s…
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan +3
Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a sha…
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan +3
Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents F…
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
Taojiannan Yang, Sijie Zhu, Matias Mendieta +6
Most existing deep neural networks are static, which means they can only do inference at a fixed complexity. But the resource budget can vary substantially across different devices…