We present a method and application for animating a human subject from a single photo. E.g., the character can walk out, run, sit, or jump in 3D. The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3D, 2) a novel 2D warping method to deform a posable template body model to fit the person’s complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions. We compare to state-of-the-art related methods and evaluate results with human studies. Further, we present an interactive interface that allows re-posing the person in 3D, and an augmented reality setup where the animated 3D person can emerge from the photo into the real world. We demonstrate the method on photos, posters, and art. The project page is at https://grail.cs. washington.edu/projects/wakeup/. Read More
Tag Archives: Image Recognition
Photo Wake-Up: 3D Character Animation from a Single Photo
MIT CSAIL’s AI can visualize objects using touch
Robots that can learn to see by touch are within reach, claim researchers at MIT’s Computer Science and Artificial Intelligence Laboratory. Really. In a newly published paper that’ll be presented next week at the Conference on Computer Vision and Pattern Recognition in Long Beach, California, they describe an AI system capable of generating visual representations of objects from tactile signals, and of predicting tactility from snippets of visual data.
“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge,” said CSAIL PhD student and lead author on the research Yunzhu Li, who wrote the paper alongside MIT professors Russ Tedrake and Antonio Torralba and MIT postdoc Jun-Yan Zhu. “By blindly touching around, our [AI] model can predict the interaction with the environment purely from tactile feelings. Bringing these two senses together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects.” Read More
Deep learning model from Lockheed Martin tackles satellite image analysis
A satellite imagery recognition system designed by Lockheed Martin engineers uses open-source deep learning libraries to quickly identify and classify objects or targets in large areas across the world. Company officials say the tool could potentially saving image analysts many man hours categorizing and labeling items within an image.
The model, Global Automated Target Recognition (GATR), runs in the cloud, using Maxar Technologies’ Geospatial Big Data platform (GBDX) to access Maxar’s 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPUs enable GATR to scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training. Read More
Detecting Kissing Scenes in a Database of Hollywood Films
Detecting scene types in a movie can be very useful for application such as video editing, ratings assignment, and personalization. We propose a system for detecting kissing scenes in a movie. This system consists of two components. The first component is a binary classifier that predicts a binary label (i.e. kissing or not) given a features exctracted from both the still frames and audio waves of a one-second segment. The second component aggregates the binary labels for contiguous non-overlapping segments into a set of kissing scenes. We experimented with a variety of 2D and 3D convolutional architectures such as ResNet, DesnseNet, and VGGish and developed a highly accurate kissing detector that achieves a validation F1 score of 0.95 on a diverse database of Hollywood films ranging many genres and spanning multiple decades. The code for this project is available at http://github.com/amirziai/kissing-detector. Read More
Does Object Recognition Work for Everyone?
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels. Read More
Watching AI Slowly Forget a Human Face Is Incredibly Creepy
A programmer created an algorithmically-generated face, and then made the network slowly forget what its own face looked like.
The result, a piece of video art titled “What I saw before the darkness,” is an eerie time-lapse view of the inside of a demented AI’s mind as its artificial neurons are switched off, one by one, HAL 9000 style. Read More
Cameras That Can See Through Walls!
50 Famous Artists Brought to Life With AI
I’m working on a longer article about democratizing AI for artists, but in the process of writing that article, I started using Runway ML and Jason Antic’s deep learning project DeOldify to colorize old black-and-white photos of artists – I couldn’t stop. So I decided to share an “eye candy” article as a preview of my longer piece.
When I was growing up, artists, and particularly twentieth century artists, were my heroes. There is something about only ever having seen many of them in black and white that makes them feel mythical and distant. Likewise, something magical happens when you add color to the photo. These icons turn into regular people who you might share a pizza or beer with.
That distance begins to collapse a bit and they come to life. Read More
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, such personalized talking head models need to be learned from a few image views of a person, potentially even a single image. Here, we present a system with such few-shot capability. It performs lengthy meta-learning on a large dataset of videos, and after that is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators. Crucially, the system is able to initialize the parameters of both the generator and the discriminator in a person-specific way, so that training can be based on just a few images and done quickly, despite the need to tune tens of millions of parameters. We show that such an approach is able to learn highly realistic and personalized talking head models of new people and even portrait paintings. Read More
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