SciFlow: Semantic Cross Interference for Self-Supervised Optical Flow Domain Generalization
Motions of objects and scenes carry essential intelligence in video understanding, offering rich cues for interpreting dynamic settings and interactions. Due to the cost and scarcity of high-quality annotation or ground truth of pixel-wise optical flow, however, motion estimation models are typically trained in synthetic domains while deployed in real-world domains. Addressing synthetic-to-real domain generalization challenges has been crucial for developing practical solutions in diverse open-world use cases. This paper introduces SciFlow, a simple yet effective, network-agnostic, training-based approach that leverages self-supervised learning to generalize motion estimation across synth...
| Authors | Jamie Menjay Lin, Jisoo Jeong, Hong Cai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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