RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method
Ming Yan Yan Zhang Shuqiang Cai Shuqi Fan Xincheng Lin Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang
Comprehensive capturing of human motions requires both accurate captures of
complex poses and precise localization of the human within scenes. Most of the
HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However,
solely using these modalities or a combination of them may not be adequate for
HPE, particularly for complex and fast movements. For holistic human motion
understanding, we present RELI11D, a high-quality multimodal human motion
dataset involves LiDAR, IMU system, RGB camera, and Event camera. It records
the motions of 10 actors performing 5 sports in 7 scenes, including 3.32 hours
of synchronized LiDAR point clouds, IMU measurement data, RGB videos and Event
steams. Through extensive experiments, we demonstrate that the RELI11D presents
considerable challenges and opportunities as it contains many rapid and complex
motions that require precise location. To address the challenge of integrating
different modalities, we propose LEIR, a multimodal baseline that effectively
utilizes LiDAR Point Cloud, Event stream, and RGB through our cross-attention
fusion strategy. We show that LEIR exhibits promising results for rapid motions
and daily motions and that utilizing the characteristics of multiple modalities
can indeed improve HPE performance. Both the dataset and source code will be
released publicly to the research community, fostering collaboration and
enabling further exploration in this field.