1 code implementation • 28 Mar 2024 • Ekkasit Pinyoanuntapong, Muhammad Usama Saleem, Pu Wang, Minwoo Lee, Srijan Das, Chen Chen
To address these challenges, we propose Bidirectional Autoregressive Motion Model (BAMM), a novel text-to-motion generation framework.
1 code implementation • 6 Dec 2023 • Ekkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, Chen Chen
MMM consists of two key components: (1) a motion tokenizer that transforms 3D human motion into a sequence of discrete tokens in latent space, and (2) a conditional masked motion transformer that learns to predict randomly masked motion tokens, conditioned on the pre-computed text tokens.
Ranked #5 on Motion Synthesis on KIT Motion-Language
no code implementations • 31 Jan 2023 • Ayman Ali, Ekkasit Pinyoanuntapong, Pu Wang, Mohsen Dorodchi
In this research, we address the challenge faced by existing deep learning-based human mesh reconstruction methods in balancing accuracy and computational efficiency.
no code implementations • 31 Jan 2023 • Ayman Ali, Ekkasit Pinyoanuntapong, Pu Wang, Mohsen Dorodchi
Recently, there has been a remarkable increase in the interest towards skeleton-based action recognition within the research community, owing to its various advantageous features, including computational efficiency, representative features, and illumination invariance.
1 code implementation • 31 Jan 2023 • Ekkasit Pinyoanuntapong, Ayman Ali, Kalvik Jakkala, Pu Wang, Minwoo Lee, Qucheng Peng, Chen Chen, Zhi Sun
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals.
1 code implementation • 27 Oct 2022 • Ekkasit Pinyoanuntapong, Ayman Ali, Pu Wang, Minwoo Lee, Chen Chen
Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities.
Ranked #6 on Multiview Gait Recognition on CASIA-B