Perception-Oriented Video Frame Interpolation via Asymmetric Blending

• URL: http://arxiv.org/abs/2404.06692v1
• Date: Wed, 10 Apr 2024 02:40:17 GMT
• Title: Perception-Oriented Video Frame Interpolation via Asymmetric Blending
• Authors: Guangyang Wu, Xin Tao, Changlin Li, Wenyi Wang, Xiaohong Liu, Qingqing Zheng,
• Abstract summary: Previous methods for Video Frame Interpolation (VFI) have encountered challenges, notably the manifestation of blur and ghosting effects. We propose PerVFI (Perception-oriented Video Frame Interpolation) to mitigate these challenges. Experimental results validate the superiority of PerVFI, demonstrating significant improvements in perceptual quality compared to existing methods.
• Score: 20.0024308216849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 \url{https://github.com/mulns/PerVFI}

Related papers

• SIGMA:Sinkhorn-Guided Masked Video Modeling [69.31715194419091]
  Sinkhorn-guided Masked Video Modelling ( SIGMA) is a novel video pretraining method. We distribute features of space-time tubes evenly across a limited number of learnable clusters. Experimental results on ten datasets validate the effectiveness of SIGMA in learning more performant, temporally-aware, and robust video representations.
  arXiv  Detail & Related papers  (2024-07-22T08:04:09Z)
• Don't Judge by the Look: Towards Motion Coherent Video Representation [56.09346222721583]
  Motion Coherent Augmentation (MCA) is a data augmentation method for video understanding. MCA introduces appearance variation in videos and implicitly encourages the model to prioritize motion patterns, rather than static appearances.
  arXiv  Detail & Related papers  (2024-03-14T15:53:04Z)
• A Contrastive Variational Graph Auto-Encoder for Node Clustering [10.52321770126932]
  State-of-the-art clustering methods have numerous challenges. Existing VGAEs do not account for the discrepancy between the inference and generative models. Our solution has two mechanisms to control the trade-off between Feature Randomness and Feature Drift.
  arXiv  Detail & Related papers  (2023-12-28T05:07:57Z)
• IBVC: Interpolation-driven B-frame Video Compression [68.18440522300536]
  B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction. Previous learned approaches often directly extend neural P-frame codecs to B-frame relying on bi-directional optical-flow estimation. We propose a simple yet effective structure called Interpolation-B-frame Video Compression (IBVC) to address these issues.
  arXiv  Detail & Related papers  (2023-09-25T02:45:51Z)
• RIGID: Recurrent GAN Inversion and Editing of Real Face Videos [73.97520691413006]
  GAN inversion is indispensable for applying the powerful editability of GAN to real images. Existing methods invert video frames individually often leading to undesired inconsistent results over time. We propose a unified recurrent framework, named textbfRecurrent vtextbfIdeo textbfGAN textbfInversion and etextbfDiting (RIGID) Our framework learns the inherent coherence between input frames in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-08-11T12:17:24Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection [52.91782218300844]
  We propose a novel Unsupervised Inconsistency-Aware method based on Vision Transformer, called UIA-ViT. Due to the self-attention mechanism, the attention map among patch embeddings naturally represents the consistency relation, making the vision Transformer suitable for the consistency representation learning.
  arXiv  Detail & Related papers  (2022-10-23T15:24:47Z)
• "I have vxxx bxx connexxxn!": Facing Packet Loss in Deep Speech Emotion Recognition [0.0]
  In applications that use emotion recognition via speech, frame-loss can be a severe issue given manifold applications. We investigate for the first time the effects of frame-loss on the performance of emotion recognition via speech.
  arXiv  Detail & Related papers  (2020-05-15T19:33:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.