Muscles in Action
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Small differences in a person's motion can engage drastically different muscles. While most visual representations of human activity are trained from video, people learn from multimodal experiences, including from the proprioception of their own muscles. We present a new visual perception task and dataset to model muscle activation in human activities from monocular video. Our Muscles in Action (MIA) dataset consists of 2 hours of synchronized video and surface electromyography (sEMG) data of subjects performing various exercises. Using this dataset, we learn visual representations that are predictive of muscle activation from monocular video. We present several models, including a transformer model, and measure their ability to generalize to new exercises and subjects. Putting muscles into computer vision systems will enable richer models of virtual humans, with applications in sports, fitness, and AR/VR.
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