https://github.com/semchan/UPST-NeRF
공식 코드UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
<span style="color:grey; opacity: 0.6">( Image credit: A Neural Algorithm of Artistic Style )</span>
The "T" horizontal strip can mimic the back of a sofa with a delicate cushion or details of the uphols or appliances with the color button.
The "T" vertical strip can show a feet or arm of the sofa, shiny, yet firm.
Put "P" next to "T", your curve to delicately with the top "T." It is intertwined. The circular part of "P" can show a cushion or a curved chair and synchronize the subject of furniture.
Make sure "P" is visually relying on "T", which reflects the relationship of cohesion and balance.
"B" can be aligned as a pair of cushions or a modern chair, with mild curves with glossy and modern aesthetics.
"I" can be a symbol of a shiny furniture or a vertical light bar and completes the shapes without overburdess them.
Color palette 4:
Includes soft soil colors such as beige, top and gray shades, along with silent or silver gold tips to touch elegance.
Consider a slope effect to enhance modernity, to keep colors elegant and complex.
Use the overlap or intertwined edges that the letters meet for the symbol of unity.
The plan should allow viewers to distinguish each letter while feeling part of the same "structure".
Use delicate geometric patterns or textures that mimic fabrics or furniture materials such as wood seeds or woven fibers.
These patterns must remain minimalist and focus on highlighting the logo, while maintaining communication.
While it deals with the subject of furniture and design, this concept conveys modernity, creativity and professional. If you like, I can create a draft design for better visualization.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
AlignMixup: Improving Representations By Interpolating Aligned Features
Educating Text Autoencoders: Latent Representation Guidance via Denoising
Artistic Enhancement and Style Transfer of Image Edges using Directional Pseudo-coloring
PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region
Detecting Text Formality: A Study of Text Classification Approaches
Exploring Cross-lingual Textual Style Transfer with Large Multilingual Language Models
Methods for Detoxification of Texts for the Russian Language
Studying the role of named entities for content preservation in text style transfer
DiffArtist: Towards Structure and Appearance Controllable Image Stylization
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
Multilingual Text Style Transfer: Datasets & Models for Indian Languages
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising
Are Large Language Models Actually Good at Text Style Transfer?
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.