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Li Yilin’s AI project blends Peking Opera characters with traditional ink‑wash painting by using PaddleHub for style transfer and PaddleGAN’s First‑Order Motion model for facial motion, then adds music and Wav2Lip lip‑sync, producing videos that modernize Chinese heritage and gauge public cultural awareness.
The article details NetEase Fuxi's research on AI‑Generated Action (AIGA) for game AI bots, covering the shift from AIGC to AIGA, advances in making bots more human‑like and stylized, and the application of RLHF fine‑tuning to bridge objective metrics with subjective player experience.
This article introduces neural style transfer, explains its underlying principles using VGG19 feature extraction, content and style loss definitions, and provides a complete PyTorch implementation with code for loading images, extracting features, computing Gram matrices, and optimizing the output image.
This article introduces ModelScope's community‑released visual models, detailing the categorization of understanding versus editing models, the two‑stage coarse‑to‑fine segmentation pipeline for matting, and four editing applications—style transfer, portrait beautification, skin enhancement, and anime‑style conversion—while also previewing upcoming sky‑replacement and video‑matting models.
This paper proposes SSAN, a novel dual‑stream network that separates content and style features to achieve domain‑generalized face anti‑spoofing, employing adversarial learning for content, contrastive learning for style, and a large‑scale evaluation protocol across twelve public datasets, achieving state‑of‑the‑art performance.
Jack Cui presents three recently open‑sourced CVPR 2022 algorithms—DualStyleGAN for high‑resolution portrait style transfer, RQ‑VAE Transformer for improved text‑to‑image generation, and VFD for deep‑fake detection—detailing their functionality, usage options, and providing links to code repositories and demo platforms.
This article explores how AI and natural language processing technologies are applied to music lyric creation, covering background challenges, rhyme retrieval methods, advanced language models such as SongNet, decoding strategies, style transfer, and a multi‑level generation platform that aims to streamline professional songwriting.
This article provides a comprehensive overview of Generative Adversarial Networks, covering their original formulation, training dynamics, loss functions, major variants such as DCGAN and WGAN, and practical implementations for image‑to‑image translation, style transfer, and handwriting font synthesis at Laiye Technology.
This article provides an in‑depth overview of Generative Adversarial Networks (GANs), covering their original formulation, major variants such as DCGAN and WGAN, challenges like mode collapse, image‑to‑image translation techniques (cGAN, pix2pix, CycleGAN), and practical handwriting style‑transfer implementations using BicycleGAN and Zi2Zi.
The Baidu ACL 2020 paper sharing live session recap presents three NLP research papers—on unsupervised style transfer, sentiment‑knowledge‑enhanced pre‑training, and conversational recommendation over multi‑type dialogs—detailing their novel models, methodologies, and key contributions.
This article explains how to build a neural style‑transfer application by preprocessing input and style images, loading a pretrained VGG16 network, defining content, style, and total‑variation losses, and finally optimizing the output image using the L‑BFGS algorithm.
Learn how to turn an AI robot into a gallery‑ready painter by setting up Docker and nvidia‑docker, installing GPU drivers, and running a neural‑style transfer pipeline that blends photographs with Monet’s brushstrokes, producing high‑quality artwork in minutes.