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Learning effective representations of visual data that generalize to avariety of downstream tasks has been a long quest for computer vision. Mostrepresentation learning approaches rely solely on visual data such as images orvideos. In this paper, we explore a novel approach, where we use humaninteraction and attention cues to investigate whether we can learn betterrepresentations compared to visual-only representations. For this study, wecollect a dataset of human interactions capturing body part movements and gazein their daily lives. Our experiments show that our "muscly-supervised"representation that encodes interaction and attention cues outperforms avisual-only state-of-the-art method MoCo (He et al.,2020), on a variety oftarget tasks: scene classification (semantic), action recognition (temporal),depth estimation (geometric), dynamics prediction (physics) and walkablesurface estimation (affordance). Our code and dataset are available at:https://github.com/ehsanik/muscleTorch.

What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions