https://github.com/zeqiang-lai/lattice
공식 코드Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
Papers with Code 아카이브
Texture Synthesis
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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/ziangcao0312/DiffTF
공식 코드DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D Generation
https://github.com/zimingzhong/MeshSegmenter
공식 코드MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
https://github.com/2024-MindSpore-1/Code10/tree/main/DRAN
DRAN: Detailed Region-Adaptive Normalization for Conditional Image Synthesis
https://github.com/AniketP04/EnhanceNet-Single-Image-Super-Resolution-Through-Automated-Texture-Synthesis
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/Archangel212/psgan-batik
https://github.com/Archangel212/psgan-pytorch
https://github.com/Asteur/paint_harmonization
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/Bhargav4488/cvmlass4
Texture Synthesis with Spatial Generative Adversarial Networks
https://github.com/DarkGeekMS/artistic-style-transfer-using-texture-synthesis
https://github.com/DmitryUlyanov/fast-neural-doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/Garfield35/Doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/Jeffwang87/Tensorflow-Based-Texture-Synthesized-Images-testing-
https://github.com/KAIST-Visual-AI-Group/SyncTweedies
Text2Tex: Text-driven Texture Synthesis via Diffusion Models
https://github.com/MQSchleich/SatelliteFAMOS
https://github.com/MindSpore-paper-code-3/code2/tree/main/CTSDG
Image Inpainting via Conditional Texture and Structure Dual Generation
https://github.com/MindSpore-paper-code-3/code8/tree/main/CTSDG
Image Inpainting via Conditional Texture and Structure Dual Generation
https://github.com/a-maumau/psgan.pytorch
https://github.com/adobe/Deep-Audio-Prior
https://github.com/advaza/enhancenet_pretrained
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/afruehstueck/tileGAN
https://github.com/ahoudard/wgenpatex
A Generative Model for Texture Synthesis based on Optimal Transport between Feature Distributions
https://github.com/alexjc/neural-doodle
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Incorporating long-range consistency in CNN-based texture generation
https://github.com/awentzonline/image-analogies
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/changjianhui/LCIC_plus
Conceptual Compression via Deep Structure and Texture Synthesis
https://github.com/chenqikkl/Qi
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/crispianm/st-mfnet-mini
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
https://github.com/cvmi-lab/point-uv-diffusion
https://github.com/danier97/st-mfnet
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
https://github.com/daveredrum/scenetex
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors
https://github.com/factoryIO/1-simple_neural_style_transfer
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/freedombenLiu/deep-painterly-harmonization
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
https://github.com/geonm/EnhanceNet-Tensorflow
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
https://github.com/google-research/self-organising-systems/blob/master/notebooks/%CE%BCNCA_jax.ipynb
$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
https://github.com/google-research/self-organising-systems/blob/master/notebooks/%CE%BCNCA_pytorch.ipynb
$μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.