Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place -- many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place--many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.

To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions.

Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user's gestures. To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people's faces or license plate numbers. And this assumes the video data are publicly available in the first place -- many datasets are owned by companies and aren't free to use. So, researchers are turning to synthetic datasets.