Papers with Code 아카이브

Texture Synthesis

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 0
보존된 평가 기록: 0

The fundamental goal of example-based Texture Synthesis is to generate a texture, usually larger than the input, that faithfully captures all the visual characteristics of the exemplar, yet is neither identical to it, nor exhibits obvious unnatural looking artifacts.

Source: Non-Stationary Texture Synthesis by Adversarial Expansion

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/danielism97/st-mfnet

공식 코드

ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

https://github.com/google-research/self-organising-systems/blob/master/notebooks/texture_nca_tf2.ipynb

공식 코드

Texture Generation with Neural Cellular Automata

https://github.com/google-research/self-organising-systems/tree/master/notebooks/

공식 코드

$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata

https://github.com/guillaumebrg/texture_generation

공식 코드

Incorporating long-range consistency in CNN-based texture generation

https://github.com/h4nwei/deepdc

공식 코드

DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator

https://github.com/hjwdzh/AdversarialTexture

공식 코드

Adversarial Texture Optimization from RGB-D Scans

https://github.com/hongfz16/avatarclip

공식 코드

AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars

https://github.com/juelin/texturesynthesis-stylegan2-pytorch

공식 코드

Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2

https://github.com/leongatys/DeepTextures

공식 코드

Texture Synthesis Using Convolutional Neural Networks

https://github.com/lifan724/magic_eraser

공식 코드

MagicEraser: Erasing Any Objects via Semantics-Aware Control

https://github.com/liping1005/longrangeslicedwasserstein

공식 코드

Long Range Constraints for Neural Texture Synthesis Using Sliced Wasserstein Loss

https://github.com/llm-lab-org/generative-ai-for-character-animation-survey

공식 코드

Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future Directions

https://github.com/m3dv/lefusion

공식 코드

LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification

공식 코드

Semantics-Aligned Representation Learning for Person Re-identification

https://github.com/ml4ai/ggan

공식 코드

Texture Generation Using A Graph Generative Adversarial Network And Differentiable Rendering

https://github.com/nbiclab/cem3dmg

공식 코드

3D microstructural generation from 2D images of cement paste using generative adversarial networks

https://github.com/ncsoft/CaPa

공식 코드

CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generation

https://github.com/nicaogr/multiresolution_texture

공식 코드

High resolution neural texture synthesis with long range constraints

https://github.com/open-mmlab/mmediting

공식 코드

Texture Memory-Augmented Deep Patch-Based Image Inpainting

https://github.com/pierrickch/textureautoencoder

공식 코드

A geometrically aware auto-encoder for multi-texture synthesis

https://github.com/qiminchen/shaddr

공식 코드

ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering

https://github.com/researchmm/AOT-GAN-for-Inpainting

공식 코드

Aggregated Contextual Transformations for High-Resolution Image Inpainting

https://github.com/researchmm/TTSR

공식 코드

Learning Texture Transformer Network for Image Super-Resolution

https://github.com/ruihangao/tactiledreamfusion

공식 코드

Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generation

https://github.com/sail-sg/sewformer

공식 코드

Towards Garment Sewing Pattern Reconstruction from a Single Image

https://github.com/shivangi-aneja/ClipFace

공식 코드

ClipFace: Text-guided Editing of Textured 3D Morphable Models

https://github.com/silviazuffi/smalst

공식 코드

Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"

https://github.com/simofoti/uv3-ted

공식 코드

UV-free Texture Generation with Denoising and Geodesic Heat Diffusions

https://github.com/tencent/hunyuan3d-2

공식 코드

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

https://github.com/tencent/tencent-xr-3dgen

공식 코드

Pandora3D: A Comprehensive Framework for High-Quality 3D Shape and Texture Generation

https://github.com/udonda/structure_guided_diffusion_model

공식 코드

A Structure-Guided Diffusion Model for Large-Hole Image Completion

https://github.com/xiaorongjun000/self-rectification

공식 코드

Generating Non-Stationary Textures using Self-Rectification

https://github.com/xiefan-guo/ctsdg

공식 코드

Image Inpainting via Conditional Texture and Structure Dual Generation

https://github.com/yixunliang/unitex

공식 코드

UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes

https://github.com/zalandoresearch/psgan

공식 코드

Learning Texture Manifolds with the Periodic Spatial GAN

https://github.com/zalandoresearch/spatial_gan

공식 코드

Texture Synthesis with Spatial Generative Adversarial Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.