Video Style Transfer
14 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Video Style Transfer
Latest papers
WAIT: Feature Warping for Animation to Illustration video Translation using GANs
giddyyupp/wait
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Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video.
Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models
weifeng-chen/control-a-video
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Based on a pre-trained conditional text-to-image (T2I) diffusion model, our model aims to generate videos conditioned on a sequence of control signals, such as edge or depth maps.
Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
Large-scale text-to-video diffusion models have demonstrated an exceptional ability to synthesize diverse videos.
Two Birds, One Stone: A Unified Framework for Joint Learning of Image and Video Style Transfers
Current arbitrary style transfer models are limited to either image or video domains.
CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
linfengWen98/CAP-VSTNet
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CVPR 2023
Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer.
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing
chenyangqiqi/fatezero
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ICCV 2023
We also have a better zero-shot shape-aware editing ability based on the text-to-video model.
VToonify: Controllable High-Resolution Portrait Video Style Transfer
Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency.
CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
CCPL can preserve the coherence of the content source during style transfer without degrading stylization.
Layered Neural Atlases for Consistent Video Editing
ykasten/layered-neural-atlases
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We present a method that decomposes, or "unwraps", an input video into a set of layered 2D atlases, each providing a unified representation of the appearance of an object (or background) over the video.
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
huage001/adaattn
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ICCV 2021
Finally, the content feature is normalized so that they demonstrate the same local feature statistics as the calculated per-point weighted style feature statistics.
