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Found 6 papers, 4 papers with code
BAMM: Bidirectional Autoregressive Motion Model
To address these challenges, we propose Bidirectional Autoregressive Motion Model (BAMM), a novel text-to-motion generation framework.
MMM: Generative Masked Motion Model
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
A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose
In this research, we address the challenge faced by existing deep learning-based human mesh reconstruction methods in balancing accuracy and computational efficiency.
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)
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.
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals.
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
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
