  Flow-guided synthesis provides a common framework for frame interpolation,
where optical flow is estimated to guide the synthesis of intermediate frames
between consecutive inputs. In this paper, we present UPR-Net, a novel Unified
Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid
framework, UPR-Net exploits lightweight recurrent modules for both
bi-directional flow estimation and intermediate frame synthesis. At each
pyramid level, it leverages estimated bi-directional flow to generate
forward-warped representations for frame synthesis; across pyramid levels, it
enables iterative refinement for both optical flow and intermediate frame. In
particular, we show that our iterative synthesis strategy can significantly
improve the robustness of frame interpolation on large motion cases. Despite
being extremely lightweight (1.7M parameters), our base version of UPR-Net
achieves excellent performance on a large range of benchmarks. Code and trained
models of our UPR-Net series are available at:
https://github.com/srcn-ivl/UPR-Net.
