We now have Google Research, based right here in Atlanta (Google Research, Atlanta) and we are hiring in computer vision, machine learning, artificial intelligence, and human-computer interaction, with a specific focus on content/video understanding and creation. Here’s a bit more info for folks who are interested I am establishing a research and advanced development team […]
Creating marketing videos from scratch can be challenging, especially when designing for multiple platforms with different viewing criteria. We present URL2Video, an automatic approach that converts a web page into a short video given temporal and visual constraints. URL2Video captures quality materials and design styles extracted from a web page, including fonts, colors, and layouts. Using constraint programming, URL2Video’s design engine organizes the visual assets into a sequence of shots and renders to a video with a user-specified aspect ratio and duration. Creators can review the video composition, modify constraints, and generate video variation through a user interface. We learned the design process from designers and compared our automatically generated results with their creation through interviews and an online survey. The evaluation shows that URL2Video effectively extracted design elements from a web page and supported designers by bootstrapping the video creation process.
Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints.
Kudos to the team from Machine Perception at Google Research that just launched the Motion Still App to generate novel photos on an iOS device. This work is in part aimed at combining efforts like Video Textures and Video Stabilization and a lot more. Source: Research Blog: Motion Stills – Create beautiful GIFs from Live […]
Paper Abstract We present a technique that uses images, videos and sensor data taken from first-person point-of-view devices to perform egocentric field-of-view (FOV) localization. We define egocentric FOV localization as capturing the visual information from a person’s field-of-view in a given environment and transferring this information onto a reference corpus of images and videos of […]
Presentation at Google I/0 2013 by Matthias Grundmann, John Gregg, and Vivek Kwatra on our Video Stabilizer on YouTube Video stabilization is a key component of YouTubes video enhancement tools and youtube.com/editor. All YouTube uploads are automatically detected for shakiness and suggested stabilization if needed. This talk will describe the technical details behind our fully […]
Paper / Citation Abstract We propose to learn pixel-level segmentations of objects from weakly labeled (tagged) internet videos. Espe cially, given a large collection of raw YouTube content, along with potentially noisy tags, our goal is to automatically generate spatiotemporal masks for each object, such as a “dog”, without employing any pre-trained object detectors. We formulate […]
Here is an excerpt from a Google Research Blog on our Video Stabilization on YouTube. Now even more improved. One thing we have been working on within Research at Google is developing methods for making casual videos look more professional, thereby providing users with a better viewing experience. Professional videos have several characteristics that differentiate […]
