A method implemented by one or more processors of a client device that facilitates touch-free interaction between one or more users and an automated assistant, the method comprising: receiving a stream of image frames that are based on output from one or more cameras of the client device; processing the image frames of the stream using at least one trained machine learning model stored locally on the client device to monitor for occurrence of both: an invocation gesture of a user captured by at least one of the image frames, and a gaze of the user that is directed toward the client device, wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the invocation gesture comprises processing first resolution versions of the image frames using a first trained machine learning model to monitor for occurrence of the invocation gesture, and wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the gaze comprises processing second resolution versions of the image frames using a second trained machine learning model to monitor for the gaze of the user that is directed toward the client device; detecting, based on the monitoring, occurrence of both: the invocation gesture, and the gaze; and in response to detecting the occurrence of both the invocation gesture and the gaze: causing at least one function of the automated assistant to be activated. receiving a stream of image frames that are based on output from one or more cameras of the client device; processing the image frames of the stream using at least one trained machine learning model stored locally on the client device to monitor for occurrence of both: an invocation gesture of a user captured by at least one of the image frames, and a gaze of the user that is directed toward the client device, wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the invocation gesture comprises processing first resolution versions of the image frames using a first trained machine learning model to monitor for occurrence of the invocation gesture, and wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the gaze comprises processing second resolution versions of the image frames using a second trained machine learning model to monitor for the gaze of the user that is directed toward the client device; an invocation gesture of a user captured by at least one of the image frames, and a gaze of the user that is directed toward the client device, wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the invocation gesture comprises processing first resolution versions of the image frames using a first trained machine learning model to monitor for occurrence of the invocation gesture, and wherein processing the image frames of the stream using the at least one trained machine learning model to monitor for the occurrence of the gaze comprises processing second resolution versions of the image frames using a second trained machine learning model to monitor for the gaze of the user that is directed toward the client device; detecting, based on the monitoring, occurrence of both: the invocation gesture, and the gaze; and the invocation gesture, and the gaze; and in response to detecting the occurrence of both the invocation gesture and the gaze: causing at least one function of the automated assistant to be activated.