Graphics and Vision

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June 6, 2025

Animation technique simulates the motion of squishy objects

The approach could help animators to create realistic 3D characters or engineers to design elastic products.

June 6, 2025

Teaching AI models the broad strokes to sketch more like humans do

SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.

May 14, 2025

Study shows vision-language models can’t handle queries with negation words

Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.

May 9, 2025

Making AI models more trustworthy for high-stakes settings

A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.

May 8, 2025

Hybrid AI model crafts smooth, high-quality videos in seconds

The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.

March 18, 2025

Department of EECS announces 2025 promotions and appointments

All promotions and appointments will take effect July 1, 2025.

February 10, 2025

Streamlining data collection for improved salmon population management

Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.

December 10, 2024

Can robots learn from machine dreams?

MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI’s potential for creating robotics training data.

December 4, 2024

A new way to create realistic 3D shapes using generative AI

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

September 30, 2024

AI pareidolia: Can machines spot faces in inanimate objects?

New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.