  Intelligent agent naturally learns from motion. Various self-supervised
algorithms have leveraged motion cues to learn effective visual
representations. The hurdle here is that motion is both ambiguous and complex,
rendering previous works either suffer from degraded learning efficacy, or
resort to strong assumptions on object motions. In this work, we design a new
learning-from-motion paradigm to bridge these gaps. Instead of explicitly
modeling the motion probabilities, we design the pretext task as a conditional
motion propagation problem. Given an input image and several sparse flow
guidance vectors on it, our framework seeks to recover the full-image motion.
Compared to other alternatives, our framework has several appealing properties:
(1) Using sparse flow guidance during training resolves the inherent motion
ambiguity, and thus easing feature learning. (2) Solving the pretext task of
conditional motion propagation encourages the emergence of kinematically-sound
representations that poss greater expressive power. Extensive experiments
demonstrate that our framework learns structural and coherent features; and
achieves state-of-the-art self-supervision performance on several downstream
tasks including semantic segmentation, instance segmentation, and human
parsing. Furthermore, our framework is successfully extended to several useful
applications such as semi-automatic pixel-level annotation. Project page:
"http://mmlab.ie.cuhk.edu.hk/projects/CMP/".
