ProMotion: Prototypes As Motion Learners

Yawen Lu, Dongfang Liu, Qifan Wang, Cheng Han, Yiming Cui, Zhiwen Cao, Xueling Zhang, Yingjie Victor Chen, Heng Fan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 28109-28119

In this work we introduce ProMotion a unified prototypical transformer-based framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it apart from current task-specific paradigms. 1. 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. 2. 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 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.