Don’t worry, these animals won’t bite! Well, actually, they will… but that’s why they’re so incredibly useful. AniBite turns nature’s fiercest predators into adorably innovative multi-purpose clips that grip and hold everything from stationery, to EDC, to toiletries.
Designed to replace hooks, hangers, and stands with something more quirky and characteristic, AniBite’s series of one-touch clips can be placed anywhere and can hold pretty much anything. Made in three variants, a bear, a leopard, and a wolf (or as I call it, direwolf), the AniBite can hold up to 300 grams (almost 9 times its weight). Just lift the head back and place your item between the jaws before pushing the head down again. AniBites can hold your toothbrush (in both vertical and horizontal ways), razor, or even stationery on your desk. Use it as an innovative spectacle-holder beside your bed, or even for your keys right near the door… or push your creative limits and have these animals manage your cables for you!
The AniBite is made from sanitary and non-toxic plastics like PETG and PCTG, and comes with a PU Gel adhesion surface on the back that can attach to pretty much any flat (or even mildly textured) surface without leaving any marks. When the adhesion goes weak, all you have to do is clean the back with water and it’s good as new! The design is practical and child-friendly with blunted teeth that won’t pierce or poke.
Ideal for the nature lover as well as the design lover, these little multi-purpose hang-clips are utilitarian, eye-catching, and go right into our hall of quirky fame! Why hang real animal heads on your wall when you could have these functional, adorable miniatures instead?
“Skillfully composed of fine lines and filled in with lifelike color, each tattoo’s style is rooted in realism. The subject matter, however, is entirely surreal. As hexagons, cubes, and bricks of skin are “removed,” unique spectacles—including galactic battles, cosmic portraits, geometric patterns, and under-the-sea scenery—emerge. To “reveal” these unexpected images from below the surface of the skin, Rix plays with perspective, experiments with light, and alters dimensionality.”
Die Gallery1988 hatte neulich eine Ausstellung voller Rick and Morty-Illus am Start und die Prints und Malereien haben sie nun online gestellt. Hier ein paar Favs:
If you’re near Paris next week and you love APIs and Automation, meet the CommitStrip team at APIDays Paris next 13-14th of December, the famous conference about APIs. Register with COMMITSTRIP code and get a 30% discount. More on apidays.io!
The theory boils down to two sentences: 1) A character is in a zone of comfort, 2) but they want something. 3) They enter an unfamiliar situation, 4) adapt to it, 5) get what they wanted, 6) pay a heavy price for it, 7) then return to their familiar situation, 8) having changed. Harmon plots those eight points along the quadrants of a circle, but then overlays that circle with great dualities like life and death, consciousness and unconsciousness, and order and chaos, and finds within that very literal geometry storytelling needs, like internal and external conflict.
“The capacity to be alone is the capacity to love. It may look paradoxical to you, but it’s not. It is an existential truth: only those people who are capable of being alone are capable of love, of sharing, of going into the deepest core of another person–without possessing the other, without becoming dependent on the other, without reducing the other to a thing, and without becoming addicted to the other. They allow the other absolute freedom, because they know that if the other leaves, they will be as happy as they are now. Their happiness cannot be taken by the other, because it is not given by the other.”
NeuralNetwork-Fuzzies am Skolkovo Institute of Science and Technology haben eine künstliche Intelligenz darauf trainiert, Bickrichtungen in Bildern zu korrigieren: DeepWarp: Photorealistic ImageResynthesis for Gaze Manipulation. Erinnert an Tom Whites Smile-Vector und ist genau wie seine Arbeit eine (noch) sehr aufwändige Spielerei, die man derzeit (noch) besser mit Photoshop hinbekommt und die (noch) nur bei Stills funktioniert. (via CreativeAI)
In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze. We treat this problem as a specific instance of conditional image generation, and suggest a new deep architecture that can handle this task very well as revealed by numerical comparison with prior art and a user study. Our deep architecture performs coarse-to-fine warping with an additional intensity correction of individual pixels.
All these operations are performed in a feed-forward manner, and the parameters associated with different operations are learned jointly in the end-to-end fashion. After learning, the resulting neural network can synthesize images with manipulated gaze, while the redirection angle can be selected arbitrarily from a certain range and provided as an input to the network.
Da es keine Datensätze für Blickrichtungen gibt, haben sie für ihre Arbeit einfach ein paar Testpersonen in eine Clockwork-Orange-Vorrichtung geklemmt und auf einen wandernden Punkt glotzen lassen:
There are no publicly available datasets suitable for the purpose of the gaze correction task with continuously varying redirection angle. Therefore, we collect our own dataset Figure 4. To minimize head movement, a person places his head on a special stand and follows with her gaze a moving point on the screen in front of the stand. While the point is moving, we record several images with eyes looking in different fixed directions (about 200 for one video sequence) using a webcam mounted in the middle of the screen. For each person we record 2 − 10 sequences, changing the head pose and light conditions between different sequences. Training pairs are collected, taking two images with different gaze directions from one sequence. We manually exclude bad shots, where a person is blinking or where she is not changing gaze direction monotonically as anticipated. Most of the experiments were done on the dataset of 33 persons and 98 sequences.
Hier eine non-algorithmische Alternative mit Kinski und Nosferatu vom großartigen Ensalada-Tumblr:
