Previous methods for Video Frame Interpolation (VFI) have encountered
challenges, notably the manifestation of blur and ghosting effects. These
issues can be traced back to two pivotal factors: unavoidable motion errors and
misalignment in supervision. In practice, motion estimates often prove to be
error-prone, resulting in misaligned features. Furthermore, the reconstruction
loss tends to bring blurry results, particularly in misaligned regions. To
mitigate these challenges, we propose a new paradigm called PerVFI
(Perception-oriented Video Frame Interpolation). Our approach incorporates an
Asymmetric Synergistic Blending module (ASB) that utilizes features from both
sides to synergistically blend intermediate features. One reference frame
emphasizes primary content, while the other contributes complementary
information. To impose a stringent constraint on the blending process, we
introduce a self-learned sparse quasi-binary mask which effectively mitigates
ghosting and blur artifacts in the output. Additionally, we employ a
normalizing flow-based generator and utilize the negative log-likelihood loss
to learn the conditional distribution of the output, which further facilitates
the generation of clear and fine details. Experimental results validate the
superiority of PerVFI, demonstrating significant improvements in perceptual
quality compared to existing methods. Codes are available at
https://github.com/mulns/PerVFI

针对视频帧插值中的模糊和幻影效果等问题，我们提出了一种名为 PerVFI（以感知为导向的视频帧插值）的新方法，采用了 Asymmetric Synergistic Blending 模块，通过从两侧提取特征进行协同融合，使用稀疏拟二进制掩膜有效减轻幻影和模糊伪影，并采用基于归一化流的生成模型和负对数似然损失来学习输出的条件分布，进一步提高生成的图像细节的清晰度。实验证明，PerVFI 相较于现有方法在感知质量上有显著改进。