1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
MS-Temba: Multi-Scale Temporal Mamba for Understanding Long Untrimmed Videos
Arkaprava Sinha, Monish Soundar Raj, Pu Wang +3
Temporal Action Detection (TAD) in untrimmed videos poses significant challenges, particularly for Activities of Daily Living (ADL) requiring models to (1) process long-duration vi…
GenHMR: Generative Human Mesh Recovery
Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang +3
Human mesh recovery (HMR) is crucial in many computer vision applications; from health to arts and entertainment. HMR from monocular images has predominantly been addressed by dete…
MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild
Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Mayur Jagdishbhai Patel +4
Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which…
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed Transformer
Wenhan Wu, Ce Zheng, Zihao Yang +3
Recently, transformers have demonstrated great potential for modeling long-term dependencies from skeleton sequences and thereby gained ever-increasing attention in skeleton action…
LLAVIDAL: A Large LAnguage VIsion Model for Daily Activities of Living
Dominick Reilly, Rajatsubhra Chakraborty, Arkaprava Sinha +5
Current Large Language Vision Models (LLVMs) trained on web videos perform well in general video understanding but struggle with fine-grained details, complex human-object interact…
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…