  In this work, we introduce ProMotion, a unified prototypical framework
engineered to model fundamental motion tasks. ProMotion offers a range of
compelling attributes that set it apart from current task-specific paradigms.
We adopt a prototypical perspective, establishing a unified paradigm that
harmonizes disparate motion learning approaches. This novel paradigm
streamlines the architectural design, enabling the simultaneous assimilation of
diverse motion information. We capitalize on a dual mechanism involving the
feature denoiser and the prototypical learner to decipher the intricacies of
motion. This approach effectively circumvents the pitfalls of ambiguity in
pixel-wise feature matching, significantly bolstering the robustness of motion
representation. We demonstrate a profound degree of transferability across
distinct motion patterns. This inherent versatility reverberates robustly
across a comprehensive spectrum of both 2D and 3D downstream tasks. Empirical
results demonstrate that ProMotion outperforms various well-known specialized
architectures, achieving 0.54 and 0.054 Abs Rel error on the Sintel and KITTI
depth datasets, 1.04 and 2.01 average endpoint error on the clean and final
pass of Sintel flow benchmark, and 4.30 F1-all error on the KITTI flow
benchmark. For its efficacy, we hope our work can catalyze a paradigm shift in
universal models in computer vision.
