MIT tool visualizes and edits “physically impossible” objects
By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
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By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.
Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.
A computer vision study compares changes in pedestrian behavior since 1980, providing information for urban designers about creating public spaces.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
A new method can physically restore original paintings using digitally constructed films, which can be removed if desired.
Coactive, founded by two MIT alumni, has built an AI-powered platform to unlock new insights from content of all types.
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.
A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.