Neural Neighbor Style Transfer
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our approach is based on explicitly replacing neural features extracted from the content input (to be stylized) with those from a style exemplar, then synthesizing the final output based on these rearranged features. While the spirit of our approach is similar to prior work, we show that our design decisions dramatically improve the final visual quality.
Code (1)
Tasks
Style TransferSimilar Papers 제목 키워드 기반
CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields
Creating artistic 3D scenes can be time-consuming and requires specialized knowledge. To address this, recent works such as ARF, use a radiance field-based approach with style constraints to generate 3D scenes that resem…
Style TransferStyleSplat: 3D Object Style Transfer with Gaussian Splatting
Recent advancements in radiance fields have opened new avenues for creating high-quality 3D assets and scenes. Style transfer can enhance these 3D assets with diverse artistic styles, transforming creative expression. Ho…
ObjectStyle TransferStyleGaussian: Instant 3D Style Transfer with Gaussian Splatting
We introduce StyleGaussian, a novel 3D style transfer technique that allows instant transfer of any image's style to a 3D scene at 10 frames per second (fps). Leveraging 3D Gaussian Splatting (3DGS), StyleGaussian achiev…
3DGSDecoderStyle TransferABC-GS: Alignment-Based Controllable Style Transfer for 3D Gaussian Splatting
3D scene stylization approaches based on Neural Radiance Fields (NeRF) achieve promising results by optimizing with Nearest Neighbor Feature Matching (NNFM) loss. However, NNFM loss does not consider global style informa…
NeRFStyle TransferCCPL: 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 Transfer