Neural Neighbor Style Transfer

Daniel Sykora; Eli Shechtman; Greg Shakhnarovich; Michal Kucera; Nicholas Kolkin; Sylvain Paris

Neural Neighbor Style Transfer

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2203.13215 v1 pith:IWIYGKDT submitted 2022-03-24 cs.CV cs.GR

Nicholas Kolkin , Michal Kucera , Sylvain Paris , Daniel Sykora , Eli Shechtman , Greg Shakhnarovich This is my paper · ORCID

classification cs.CVcs.GR

keywords styleneuraltransferapproachfeaturesfinalneighborquality

verification ladder T0 review T1 audit T2 compute T3 formal

0 comments

read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion
cs.CV 2024-12 conditional novelty 6.0 of 10

A single edited image is used to fine-tune InstructPix2Pix, which then guides a two-stage optimization of deformable 3D Gaussians for consistent, controllable dynamic 3D scene editing.

Pith tools