# STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation

arXiv: https://arxiv.org/abs/2007.05481 · HTML: https://mlsanity.com/paper/2007.05481/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 12 of 12)

- [VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow Estimation](https://mlsanity.com/paper/2303.08340/) (2023)
- [DELTA: Dense Efficient Long-range 3D Tracking for any video](https://mlsanity.com/paper/2410.24211/) (2024)
- [Dense Continuous-Time Optical Flow from Events and Frames](https://mlsanity.com/paper/2203.13674/) (2022)
- [Self-Supervised Multi-Frame Monocular Scene Flow](https://mlsanity.com/paper/2105.02216/) (2021)
- [AccFlow: Backward Accumulation for Long-Range Optical Flow](https://mlsanity.com/paper/2308.13133/) (2023)
- [DELTAv2: Accelerating Dense 3D Tracking](https://mlsanity.com/paper/2508.01170/) (2025)
- [An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain](https://mlsanity.com/paper/2303.13077/) (2023)
- [Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling](https://mlsanity.com/paper/2304.07159/) (2023)
- [Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction](https://mlsanity.com/paper/2207.03790/) (2022)
- [Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network](https://mlsanity.com/paper/2305.09156/) (2023)
- [CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices](https://mlsanity.com/paper/2412.13273/) (2024)
- [Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes](https://mlsanity.com/paper/2605.29220/) (2026)

## Abstract (arXiv)

We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation. Our solution introduces a double recurrence over spatial scale and time through repeated use of a generic "STaR" (SpatioTemporal Recurrent) cell. It includes (i) a temporal recurrence based on conveying learned features rather than optical flow estimates; (ii) an occlusion detection process which is coupled with optical flow estimation and therefore uses a very limited number of extra parameters. The resulting STaRFlow algorithm gives state-of-the-art performances on MPI Sintel and Kitti2015 and involves significantly less parameters than all other methods with comparable results.