Image based virtual try on network from unpaired data
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This document summarizes an image-based virtual try-on network that can synthesize multiple garments onto a person from unpaired data using GANs. The network uses a geometric matching module to generate segmentation maps and DensePose to map pixels to 3D surfaces. It then uses an appearance generation network inspired by SwapNet along with online optimization to accurately synthesize textures, logos, and embroidery. Evaluation metrics include FID, IS, and human evaluations, demonstrating the ability to realistically render outfits from single images.
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Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
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Nomination are Open!! Don't Miss it
Visit: computer.scifat.com
Award Nomination: https://x-i.me/ishnom
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- 1. Image Based Virtual Try-on Network from Unpaired Data CVPR 2020 2020.09.21 백 상 원 국민대학교 HCI LAB
- 2. Abstract • Fashion Outfit algorithm • Low cost compare to other method using 3d information • GAN , Style transfer , conditional GAN, etc … • Online fitting available
- 3. Introduction 3D fitting system Deep Fashion3D: Dataset & Benchmark for Virtual Clothing Try-On and More
- 4. Introduction • Low computation cost • 3D methods vs O-VITION • Conditional image generation methods
- 5. Novelty • Multiple garment synthesis. • Accurately synthesizes fine garment features like textures, logos and embroidery • Online optimization
- 6. Geometric Matching module • Grid Interval Consistency Loss • Convolutional neural network architecture for geometric matching CVPR 2017 Jae Lee, Hyug, et al. "LA-VITON: A Network for Looking-Attractive Virtual Try-On." Proceedings of the IEEE International Conference on Computer Vision Workshops. 2019.
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