A new research paper from the Hong Kong University of Science and Technology (HKUST) and Microsoft Research offers a rational and less resource-intensive way to generate 3D representations of people, by combining latent diffusion and NeRF-based approaches into a single framework.
New research from Korea and the United States has proposed an integrated method for preventing text-to-image systems such as Stable Diffusion from generating ‘immoral’ images – by manipulating the generative processes within the system to intercept ‘controversial’ content and transform the generated content into what the authors characterize as ‘morally-satisfying’ images instead.
If time were no object, Neural Radiance Fields (NeRF) might by now have made greater inroads into potential commercial implementations – particularly in the field of human avatars and facial recreation.
New research from the University of California at Berkeley improves notably on recent efforts to create AI-powered image-editing procedures – this time by combining the considerable calculative forces of OpenAI’s GPT-3 Natural Language Processing (NLP) model with the latest version of Stability.ai’s world-storming Stable Diffusion text-to-image and image-to-image latent diffusion architecture.
In the 2014 Christmas special of the dystopian sci-fi anthology series Black Mirror, writer and series creator Charlie Brooker envisaged the possibility of cybernetic augmentations capable of ‘blocking out’ upsetting or banned material for a particular user – not only online, but in real-world interactions. In the episode, one of the main characters was ‘blocked’ by his ex-wife, so that she could literally no longer ‘see’ him.
A new collaboration between Binghamton University and Intel offers a novel take on the problem of the unauthorized use of social media photos for facial recognition purposes, as well as AI and deepfake training – by using deepfake techniques to subtly alter the appearance of people in posted photos, so that only their friends and permitted contacts are able to see their original face images.
Besides their capacity to rip off the style of popular real world artists, the new breed of latent diffusion- based image synthesis systems promises a revolutionary ease-of-use not only for valid creative purposes, such as concept art development and stock image generation, but also for creating controversial deepfakes, and potentially objectionable imagery, including child pornography.
New research from Tel Aviv University may prove capable of bringing authentic human motion to text-to-video synthesis, videogames, motion capture architectures in VFX pipelines, and also function as a synthetic data generator for downstream research initiatives, among a myriad of other potential applications – all thanks to what has turned out to be the most sensational AI technology of 2022 – diffusion models.
A researcher from Spain has developed a new method for users to generate their own styles in Stable Diffusion (or any other latent diffusion model that is publicly accessible) without fine-tuning the trained model or needing to gain access to exorbitant computing resources, as is currently the case with Google’s DreamBooth and with Textual Inversion – both methods which are primarily intended to insert objects or people into the Stable Diffusion universe, rather than impose environmental ambience or styles (i.e. ‘in the style of Van Gogh/Kubrick/Mapplethorpe’, etc.).
It’s nearly five years since the advent of deepfakes released into the public realm the ability to alter people’s facial identities; at first, in recorded video, and now even as a streaming implementation, with DeepFaceLive.
