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Real-world objects perform complex motions that involve multiple independentmotion components. For example, while talking, a person continuously changestheir expressions, head, and body pose. In this work, we propose a novel methodto decompose motion in videos by using a pretrained image GAN model. Wediscover disentangled motion subspaces in the latent space of widely usedstyle-based GAN models that are semantically meaningful and control a singleexplainable motion component. The proposed method uses only a few $(\approx10)$ground truth video sequences to obtain such subspaces. We extensively evaluatethe disentanglement properties of motion subspaces on face and car datasets,quantitatively and qualitatively. Further, we present results for multipledownstream tasks such as motion editing, and selective motion transfer, e.g.transferring only facial expressions without training for it.

We never go out of Style: Motion Disentanglement by Subspace Decomposition of Latent Space