The Next AI Blog

Stay informed with the latest updates on synthetic data.

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Introducing Our 50K Synthetic Identities!

We are pleased to announce that 50K face new identities have been integrated into the Datagen product and are accessible through our SDK and API!  Training and testing with a large number of unique identities can be a key to good results. That’s why we’ve focused on continuously broadening and expanding the diversity and scale of the...
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Custom Structure Preservation in Face Aging

Introduction Face age transformation (editing) involves reflecting age factors on a given face to create its future or past face images. A transformed face image must retain the identity of the original photo subject and all other details related to the face image.  Over the last few years this problem has attracted increasing attention both...
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Highlights from ECCV 2022, Part II

In our last blog, we published 3 of our 6 highlighted papers from ECCV 2022 in part I. We reviewed 3D-Aware Indoor Scene Synthesis with Depth Priors, Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors and Physically-Based Editing of Indoor Scene Lighting from a Single Image here. Keep reading to see the last three: 1. Text2LIVE:...
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Breakthroughs in Synthetic Data from NeurIPS 2022

Last year, we reported five top papers from NeurIPS 2021 in the field of synthetic data technology. NeurIPS is back this year, with more exciting developments. Here, we round up five noteworthy breakthroughs from NeurIPS 2022. 1. TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies It is no secret that one can use 360-degree...
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Highlights from ECCV 2022, Part I

The European Conference on Computer Vision (ECCV) 2022 did not disappoint. The 17th rendition of ECCV was jam packed with discoveries in the different facets of computer vision, ranging from generative adversarial networks (GANS) to visual transformers, from object detection to scene reconstruction.  From the treasure trove of papers, we handpicked six exciting papers from...
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DigiFace-1M: 1 Million Digital Face Images

This is the first part in our series on facial recognition Microsoft generated quite a bit of buzz this month when it released the DigiFace1M, a large-scale synthetic dataset for face recognition. It is akin to a refreshing breath of air when compared to existing large-scale image datasets – it is generated without privacy violations and consent issues...
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The Next Frontier: Computer Vision on 3D Data

Or Litany currently works as a senior research scientist at Nvidia. He earned his BSC in physics and mathematics from Hebrew University and his master’s degree from the Technion. After that, he went on to do his PhD at Tel Aviv University, where he worked on analyzing 3D data with graph neural networks. For his...
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Dual-Generator Face Reenactment

Introduction Face reenactment (FR) is a conditional face generation technique aiming to accomplish two objectives given a pair of face images (source and reference faces):  1) Translate an expression and pose of a reference face (=face shape) into a target face. 2) Transfer an identity of a source face into a target face. The goal...
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The Future of Foundation Models

This is part 3 of a series of blogs on Foundation Models and their impact on AI. You can read part 1 and part 2 here and here.   The  risks of foundation models (see part 2) are Gordian Knots that must be addressed before they pose a danger to society. Given our limited understanding of foundation models, it is already...
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