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We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Observing that 3D supervision alone is not sufficient for high fidelity color reconstruction, we introduce patch-based rendering losses that enable reliable color reconstruction on visible parts of the human, and detailed and plausible color estimation for the non-visible parts. Moreover, our method specifically addresses methodological and practical limitations of prior work in terms of representing geometry, albedo, and illumination effects, in an end-to-end model where factors can be effectively disentangled. In extensive experiments, we demonstrate the versatility and robustness of our approach.

I was standing off to the side of the showroom at CES while engineers worked in hushed voices, fussing over a $16,000 artificial intelligence-powered laundry-folding machine. The machine wasn't giving back the T-shirt I put in, and for one brief, terrifying second, I really thought I broke it. I had brought my own Verge T-shirt to try out a prototype of Laundroid, and I had to coax Seven Dreamers CEO Shin Sakane into letting me drop my shirt in, instead of the demo shirts they had prepared. As he expected, it didn't work. After about 15 minutes, the Laundroid opened up to reveal nothing but an empty drawer.

I was standing off to the side of the showroom at CES while engineers worked in hushed voices, fussing over a $16,000 artificial intelligence-powered laundry-folding machine. The machine wasn't giving back the T-shirt I put in, and for one brief, terrifying second, I really thought I broke it. I had brought my own Verge T-shirt to try out a prototype of Laundroid, and I had to coax Seven Dreamers CEO Shin Sakane into letting me drop my shirt in, instead of the demo shirts they had prepared. As he expected, it didn't work. After about 15 minutes, the Laundroid opened up to reveal nothing but an empty drawer.

On the third floor of a shopping mall in the heart of Shanghai last week, Xiaolan He, a woman in her 50s, took an olive-green down jacket to a fitting room. To her surprise, she found a screen about the size of a large poster on the wall. It recognized the item of clothing in her hands through a tiny sensor embedded in the garment, and showed several options for matching items that she could flip through like a photo album. The screen, and the system that powers it, make up FashionAI--which essentially became He's personal stylist. FashionAI received its first big wave of customers on Saturday during Singles' Day, a Chinese shopping festival started by Alibaba in 2009 and held on November 11 each year.

Artificial intelligence might just spawn a whole new style trend: call it "predictive fashion." In a paper published on the ArXiv, researchers from the University of California, San Diego, and Adobe have outlined a way for AI to not only learn a person's style but create computer-generated images of items that match that style. The system could let retailers create personalized pieces of clothing, or could even be used to help predict broader fashion trends. First, the researchers trained a convolutional neural network (CNN) to learn and classify a user's preferences for certain items, using purchase data scraped from Amazon in six categories: shoes, tops, and pants for both women and men. This type of recommender model is common in the online retail world, usually showing up in an "Other items you might like" area at the bottom of a page.

Online fashion tech startup Vue.ai is selling technology that analyzes pieces of clothing and automatically generates an image of the garment on a person of any size, shape, or wearing any kind of shoes. Neural networks, the technology that GANs are built on, are an approximation of how our brain works: Millions of tiny, distributed neurons, processing data and passing them along to the next neuron. These networks are trained on thousands of images, and the neurons learn to distinguish different kinds of elbows, hips, and colors. Through trial and error, the two engineers figured out exactly the right neurons to alter the size, weight, or shape of a person, or the hardest part, the shoes they're wearing.

The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.

The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.

The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail--making recommendations based on products popping up in social-media posts, for instance. For instance, one group of Amazon researchers based in Israel developed machine learning that, by analyzing just a few labels attached to images, can deduce whether a particular look can be considered stylish. An Amazon team at Lab126, a research center based in San Francisco, has developed an algorithm that learns about a particular style of fashion from images, and can then generate new items in similar styles from scratch--essentially, a simple AI fashion designer. The event included mostly academic researchers who are exploring ways for machines to understand fashion trends.