Agencies bill top dollar for human assets. Photographers, hair, and makeup all get paid by the hour – in addition to ancillary (but often substantial) costs like travel. We eliminate middlemen and excess cost. We bill only for images you need and not those that you do not. That translates to a superior cost structure (with greater speed and precision).
Simply put: we achieve print quality image results comparable to those obtainable from actual human models and incumbent agencies. In extensive testing / polling we’ve conducted, our models cannot be distinguished from actual humans – even when poll recipients are told directly that an image has been rendered by AI.
Bloom assets can be redeployed perpetually in an unlimited array of scenarios. Once you have licensed a Bloom model, you can present that model in an infinite number of permutations – altering clothing, posture, or background at will.
Because Bloom assets are not fixed assets (versus a regular picture of a real model), they are all infinitely customizable. If any alteration needs to be made, we can do so effortlessly.
To err is human – and unfortunately models and agencies are no exception. We’ve come across a staggering array of foibles and shortfalls in dealing with human models – from incompetently executed photoshoots to TMZ-worthy public meltdowns. Digitized creative assets mitigate the risk and potential drama that can not only render a photoshoot ineffective (or worse) but also pose significant PR risks for the brand equity you’ve established.
Your Bloom assets are yours to retain forever. That means full control over some of your most important customer-facing intellectual property.
Bloom uses generative modeling to create photos of people that look like they were taken by a human photographer.
This is orchestrated by a series of neural networks working together to create photorealistic people and scenes that don't exist. A similar technology is being used for deepfakes.
In addition, computer vision and supervised learning for labeling now can outperform humans in tasks ranging from facial recognition to deep imagination.
Used in self-driving cars, robotics, and beating humans at Go, deep reinforcement learning explores actions and scenarios, searching over space optimally.
This enables Bloom to optimally search through the output of generative models.