Instance segmentation demands substantial labeling resources. This has prompted increased interest to explore the object discovery task as an unsupervised alternative. In particular, promising results were achieved in localizing instances using motion supervision only. However, the motion signal introduces complexities due to its inherent noise and sparsity, which constrains the effectiveness of current methodologies. In the present paper we propose DIOD (self DIstillation meets Object Discovery), the first method that places the motion-guided object discovery within a framework of continuous improvement through knowledge distillation, providing solutions to existing limitations (i) DIOD robustly eliminates the noise present in the exploited motion maps providing accurate motion-supervision (ii) DIOD leverages the discovered objects within an iterative pseudo-labeling framework, enriching the initial motion-supervision with static objects, which results in a cost-efficient increase in performance. Through experiments on synthetic and real-world datasets, we demonstrate the benefits of bridging the gap between object discovery and distillation, by significantly improving the state-of-the-art. This enhancement is also sustained across other demanding metrics so far reserved for supervised tasks. Code available upon acceptance.