Papers Video Style Transfer
“Video Style Transfer” 태그가 달린 논문 35편 · 필터 해제
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging
Video style transfer aims to alter the style of a video while preserving its content. Previous methods often struggle with content leakage and style misalignment, particularly when using image-driven approaches that aim …
DenoisingStyle TransferVideo Style TransferSingle Trajectory Distillation for Accelerating Image and Video Style Transfer
Diffusion-based stylization methods typically denoise from a specific partial noise state for image-to-image and video-to-video tasks. This multi-step diffusion process is computationally expensive and hinders real-world…
Style TransferVideo Style TransferStyleMaster: Stylize Your Video with Artistic Generation and Translation
Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observ…
Contrastive LearningStyle TransferTranslationVideo Generation+1UniVST: A Unified Framework for Training-free Localized Video Style Transfer
This paper presents UniVST, a unified framework for localized video style transfer based on diffusion model. It operates without the need for training, offering a distinct advantage over existing diffusion methods that t…
Style TransferVideo EditingVideo Style TransferNoise Crystallization and Liquid Noise: Zero-shot Video Generation using Image Diffusion Models
Although powerful for image generation, consistent and controllable video is a longstanding problem for diffusion models. Video models require extensive training and computational resources, leading to high costs and lar…
Image GenerationStyle TransferVideo GenerationVideo Style TransferCtrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. However, when it comes to controllable video ge…
GPUImage GenerationStyle TransferVideo Editing+2LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Previous work has shown that well-crafted adversarial perturbations can threaten the security of video recognition systems. Attackers can invade such models with a low query budget when the perturbations are semantic-inv…
Adversarial AttackStyle TransferVideo RecognitionVideo Style TransferWAIT: Feature Warping for Animation to Illustration video Translation using GANs
In this paper, we explore a new domain for video-to-video translation. Motivated by the availability of animation movies that are adopted from illustrated books for children, we aim to stylize these videos with the style…
Image-to-Image TranslationOptical Flow EstimationStyle TransferTranslation+1Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approach
Photorealistic style transfer aims to apply stylization while preserving the realism and structure of input content. However, existing methods often encounter challenges such as color tone distortions, dependency on pair…
GPUStyle TransferSuper-ResolutionVideo Style TransferControl-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video generation remains challenging, with exis…
Image GenerationOptical Flow EstimationStyle TransferText-to-Video Generation+3Style-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. However, due to the lack of extensive text-to-video datasets and the necessary computational resources for…
DenoisingStyle TransferVideo Style TransferTwo 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. In order to achieve satisfying image and video style transfers, two different models are inevitably required with separate training pr…
Computational EfficiencyStyle TransferVideo Style TransferCAP-VSTNet: Content Affinity Preserved Versatile Style Transfer
Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a …
Image MattingStyle TransferVideo Style TransferFateZero: Fusing Attentions for Zero-shot Text-based Video Editing
The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models f…
AttributeText-to-Video EditingVideo EditingVideo Style TransferColoristaNet for Photorealistic Video Style Transfer
Photorealistic style transfer aims to transfer the artistic style of an image onto an input image or video while keeping photorealism. In this paper, we think it's the summary statistics matching scheme in existing algor…
Optical Flow EstimationStyle TransferVideo Style TransferVToonify: Controllable High-Resolution Portrait Video Style Transfer
Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN ha…
Face AlignmentStyle TransferVideo Style TransferVocal Bursts Intensity PredictionCCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
In this paper, we aim to devise a universally versatile style transfer method capable of performing artistic, photo-realistic, and video style transfer jointly, without seeing videos during training. Previous single-fram…
Image-to-Image TranslationStyle TransferVideo Style TransferLayered Neural Atlases for Consistent Video Editing
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. For each pixel in…
Style TransferVideo EditingVideo ReconstructionVideo Style TransferAdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Fast arbitrary neural style transfer has attracted widespread attention from academic, industrial and art communities due to its flexibility in enabling various applications. Existing solutions either attentively fuse de…
Style TransferVideo Style TransferStylizing 3D Scene via Implicit Representation and HyperNetwork
In this work, we aim to address the 3D scene stylization problem - generating stylized images of the scene at arbitrary novel view angles. A straightforward solution is to combine existing novel view synthesis and image/…
NeRFNovel View SynthesisStyle TransferVideo Style Transfer