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Adobe Research scientists have helped create a new “redirected walking” technology for virtual reality that makes a tiny room feel huge.

New work by an Adobe Research scientist and collaborators shows a promising approach to detect tampered images.

The third and final post in a series on the history of image stylization explores using neural networks to stylize images.

This post, part two of a three-part series, delves into patch-based methods for image stylization.

Adobe Research shared our latest computer vision discoveries at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), held this June in Salt Lake City. Attracting an international audience of scholars and industry representatives, CVPR is one of the premier events for showcasing

This three-part series of blog posts tells the story (so far) of image stylization algorithms. This first post delves into some of the earliest stylization methods: procedural filters.

Scientists asked: Can deep learning distinguish subtle differences between images more like humans do? The answer: Yes. Even self-supervised neural networks work better than prior algorithms designed for this purpose.

Researchers used neural networks to create realistic sound for videos from scratch. The system’s soundtracks fooled humans into thinking they were real.

Can AI help marketers improve video ads, increasing their effectiveness with specific audiences? Adobe Research scientists asked this big question—and have designed a novel technology to answer it.

Adobe has achieved a remarkable acceptance rate for research papers at several highly-selective conferences this spring. These conferences serve as key platforms for sharing breakthroughs with the research community.

Sonic Scape helps 360 video editors “see” ambisonic sounds in a video using color particles, enabling a unique style of visual editing of the sound’s origin in space and time.