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Virtual Try-on
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Virtual try-on of clothing or other items such as glasses and makeup. Most recent techniques use Generative Adversarial Networks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/siqi0905/gardiff
공식 코드Improving Virtual Try-On with Garment-focused Diffusion Models
https://github.com/soon-yau/visconet
공식 코드ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNet
https://github.com/takesukeds/hyb-viton
공식 코드HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and Implicit Warping
https://github.com/thmoa/videoavatars
공식 코드https://github.com/vton-handfit/vton-handfit
공식 코드VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding
https://github.com/xiezhy6/gp-vton
공식 코드GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning
https://github.com/xiezhy6/pasta-gan
공식 코드Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
https://github.com/xthan/VITON
공식 코드https://github.com/xyhanHIT/PL-VTON
공식 코드https://github.com/xyhanhit/scw-vton
공식 코드https://github.com/xyzhouo/casd
공식 코드Cross Attention Based Style Distribution for Controllable Person Image Synthesis
https://github.com/yadiraf/scarf
공식 코드Capturing and Animation of Body and Clothing from Monocular Video
https://github.com/yifyang993/hilo
공식 코드https://github.com/yisol/IDM-VTON
공식 코드Improving Diffusion Models for Authentic Virtual Try-on in the Wild
https://github.com/zczcwh/gtrs
공식 코드A Lightweight Graph Transformer Network for Human Mesh Reconstruction from 2D Human Pose
https://github.com/zengjianhao/cat-dm
공식 코드CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion Model
https://github.com/zhenchenwan/ted-viton
공식 코드TED-VITON: Transformer-Empowered Diffusion Models for Virtual Try-On
https://github.com/zheng-chong/catv2ton
공식 코드CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation
https://github.com/zheng-chong/catvton
공식 코드CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
https://github.com/AemikaChow/DATASOURCE
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
https://github.com/Aliasgarsaifee/HackInIndia
https://github.com/EmergingUnicorns/DeepPaint
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
https://github.com/HaroldChen19/GaussianVTON
https://github.com/JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving
https://github.com/JaZz-9/Try-First
https://github.com/JaZz-9/Try-First-Flask-
https://github.com/MindCode-4/code-5/tree/main/vit
https://github.com/MindSpore-scientific-2/code-7/tree/main/WildVidFit
WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models
https://github.com/MosbehBarhoumi/VITON-PRE-PROCESSING
https://github.com/Sainzerjj/FitDiT
FitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on
https://github.com/TureganoJose/ARCproject
https://github.com/ZaiqiangWu/viton_rtl
Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments
https://github.com/ZhengZerong/DeepHuman
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
https://github.com/adldotori/ClothFlow
https://github.com/aimagelab/multimodal-garment-designer
Multimodal-Conditioned Latent Diffusion Models for Fashion Image Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.