AI Verse
Aleksandra Kiesiak is an experienced marketing professional currently serving as the Head of Marketing | Go To Market at AI Verse, specializing in the commercialization of synthetic image datasets for AI computer vision model training. Previously, Aleksandra held the position of Head of Growth & Marketing at Echo Analytics and has a diverse background at Google, including roles as EMEA Onboarding Project Manager, Digital Marketing Strategist (MMS) Lead Gen, and Account Strategist. Aleksandra's experience also includes a stint as a Marketing Specialist at SoftBank Robotics UK Ltd and as a Growth Marketing Associate at Evatronix S.A., focusing on marketing strategies for 3D scanners. Educational qualifications include a Master's degree in Management from ESCP Business School and Licentiate degrees in International Business and Sinology from Poznan University of Economics and Business and Uniwersytet im. Adama Mickiewicza w Poznaniu, respectively.
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AI Verse
AI Verse offers a self-service image factory that produces high-quality annotated synthetic datasets for the needs of computer vision engineers. An entirely novel process enables the user to describe their ideal dataset and launch its generation on AI Verse’s render farm, a scalable cloud-hosted cluster of GPU machines, each able to procedurallybuild a 3D scene and render photorealistic images in a few seconds.The dataset builder offers simple but powerful inputs for scene description, lighting and camera placement, emulating the work of a 3D artist in just a few clicks. The user specifies a desired type of environment (e.g. living room, bedroom, office) and chooses object classes of interest from a catalog of 1000+ assets in ongoing expansion. These inputs are enough to procedurally generate any desired number of 3D scenes, respecting user-defined constraints while offering the variations in appearance and content necessary to AI robustness on tasks such as object detection and semantic segmentation. The user can also specify activities to be performed by human agents in the scenes, ranging from simple postures to leisure or work-related activities (e.g. typing on a keyboard, watching TV).A wide range of lighting scenarios can be applied in order to simulate varying weather and time of the day. The placement of the camera is also randomized according to simple and effective constraints that can be defined according to the engineer’s use case. Sensor parameters (e.g lens parameters, depth-of-field, exposure) can also be adjusted.The process of setting and adjusting the scene and image parameters is made interactive and engaging through the use of live previews, allowing the user to visualize within a few seconds an image rendered on-the-fly on one of our GPU machines, corresponding to the input parameters. Availability of GPU machines for previews is guaranteed though a session-booking system.Once satisfied with their inputs, the user can launch the generation of any desired number of 3D scenes and images captured from those scenes, along with their choice of automatically generated labels (e.g. object 2D/3D boxes, instance masks, depth image). Parallelized rendering on the cloud enables delivery of thousands of images in just a couple hours. The dataset management UI enables the engineer to track progress on any of their dataset orders, and visualize a sample of images from a given dataset as soon as it is available. Once the dataset is completed, the user can download it from the cloud by generating an expiring link whenever needed, which enhances data privacy. The downloadable dataset is presented in a format that makes it ready for AI training and easy to combine with other datasets.