mlsanityData to 14 Oct 2026

Interactive Control over Temporal Consistency while Stylizing Video Streams

arXiv 2301.00750 ↗First posted 2 Jan 2023cs.GR cs.CVcited by 1

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Abstract (arXiv)

Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain-agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real-time at full HD resolutions while providing interactive consistency control. We develop a lite optical-flow network that operates at 80 FPS on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state-of-the-art video consistency approaches.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 78 references that are in the map. First posted on arXiv 2 Jan 2023.

Builds on

33 of 78 references.

References · 33

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  6. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  7. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  8. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,415
  9. 2017Decoupled Weight Decay Regularizationcited by 1,831
  10. 2016Pruning Filters for Efficient ConvNetscited by 1,143
  11. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  12. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 968
21 more
  1. 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 957
  2. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  3. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 784
  4. 2016Optical Flow Estimation using a Spatial Pyramid Networkcited by 424
  5. 2017Universal Style Transfer via Feature Transformscited by 354
  6. 2018Learning Blind Video Temporal Consistencycited by 253
  7. 2021Learning to Estimate Hidden Motions with Global Motion Aggregationcited by 224
  8. 2017Deep Bilateral Learning for Real-Time Image Enhancementcited by 200
  9. 2017Neural Style Transfer: A Reviewcited by 199
  10. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  11. 2021AutoFlow: Learning a Better Training Set for Optical Flowcited by 102
  12. 2017Coherent Online Video Style Transfercited by 96
  13. 2020Arbitrary Video Style Transfer via Multi-Channel Correlationcited by 84
  14. 2019A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularizationcited by 76
  15. 2016Fast Optical Flow using Dense Inverse Searchcited by 71
  16. 2017Artistic style transfer for videos and spherical imagescited by 45
  17. 2020Interactive Video Stylization Using Few-Shot Patch-Based Trainingcited by 43
  18. 2022Disentangling Architecture and Training for Optical Flowcited by 32
  19. 2017Characterizing and Improving Stability in Neural Style Transfercited by 31
  20. 2021Learning Long-Term Style-Preserving Blind Video Temporal Consistencycited by 5
  21. 2021STALP: Style Transfer with Auxiliary Limited Pairingcited by 2

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