Voice profiling aims at inferring various human parameters from their speech, e.g.

Voice profiling aims at inferring various human parameters from their speech, e.g. In this paper, we address the challenge posed by a subtask of voice profiling - reconstructing someone's face from their voice. The task is designed to answer the question: given an audio clip spoken by an unseen person, can we picture a face that has as many common elements, or associations as possible with the speaker, in terms of identity? To address this problem, we propose a simple but effective computational framework based on generative adversarial networks (GANs). The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set.

Generative Networks have proved to be extremely effective in image restoration and reconstruction in the past few years. Generating faces from textual descriptions is one such application where the power of generative algorithms can be used. The task of generating faces can be useful for a number of applications such as finding missing persons, identifying criminals, etc. This paper discusses a novel approach to generating human faces given a textual description regarding the facial features. We use the power of state of the art natural language processing models to convert face descriptions into learnable latent vectors which are then fed to a generative adversarial network which generates faces corresponding to those features. While this paper focuses on high level descriptions of faces only, the same approach can be tailored to generate any image based on fine grained textual features.

Marcelo Rinesi remembers what it was like to watch Jurassic Park for the first time in a theater. The dinosaurs looked so convincing that they felt like the real thing, a special effects breakthrough that permanently shifted people's perception of what's possible. After two weeks of testing DALL-E 2, the CTO of the Institute for Ethics and Emerging Technologies thinks AI might be on the verge of its own Jurassic Park moment. Last month, OpenAI introduced the second-generation version of DALL-E, an AI model trained on 650 million images and text captions. It can take in text and spit out images, whether that's a "Dystopian Great Wave off Kanagawa as Godzilla eating Tokyo" or "Teddy bears working on new AI research on the moon in the 1980s."

Marcelo Rinesi remembers what it was like to watch Jurassic Park for the first time in a theater. The dinosaurs looked so convincing that they felt like the real thing, a special effects breakthrough that permanently shifted people's perception of what's possible. After two weeks of testing DALL-E 2, the CTO of the Institute for Ethics and Emerging Technologies thinks AI might be on the verge of its own Jurassic Park moment. Last month, OpenAI introduced the second generation version of DALL-E, an AI model trained on 650 million images and text captions. It can take in text and spit out images, whether that's a "Dystopian Great Wave off Kanagawa as Godzilla eating Tokyo" or "Teddy bears working on new AI research on the moon in the 1980s."

Voice profiling aims at inferring various human parameters from their speech, e.g. In this paper, we address the challenge posed by a subtask of voice profiling - reconstructing someone's face from their voice. The task is designed to answer the question: given an audio clip spoken by an unseen person, can we picture a face that has as many common elements, or associations as possible with the speaker, in terms of identity? To address this problem, we propose a simple but effective computational framework based on generative adversarial networks (GANs). The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set.

With the help of artificial intelligence, you can manipulate video of public figures to say whatever you like -- or now, create images of people's faces that don't even exist. You can see this in action on a website called thispersondoesnotexist.com. It uses an algorithm to spit out a single image of a person's face, and for the most part, they look frighteningly real. Hit refresh in your browser, and the algorithm will generate a new face. Again, these people do not exist.

Face recall is a basic human cognitive process performed routinely, e.g., when meeting someone and determining if we have met that person before. Assisting a subject during face recall by suggesting candidate faces can be challenging. One of the reasons is that the search space - the face space - is quite large and lacks structure. A commercial application of face recall is facial composite systems - such as Identikit, PhotoFIT, and CD-FIT - where a witness searches for an image of a face that resembles his memory of a particular offender. The inherent uncertainty and cost in the evaluation of the objective function, the large size and lack of structure of the search space, and the unavailability of the gradient concept makes this problem inappropriate for traditional optimization methods. In this paper we propose a novel evolutionary approach for searching the face space that can be used as a facial composite system. The approach is inspired by methods of Bayesian optimization and differs from other applications in the use of the skew-normal distribution as its acquisition function. This choice of acquisition function provides greater granularity, with regularized, conservative, and realistic results.