Microsoft Research Lab – Asia
News & features
In the news | The Korea Times
IITP, Microsoft collaborate to build global network for fostering AI workers
In the news | VentureBeat
Microsoft’s new AI framework trains powerful reasoning models with a fraction of the cost
RenderFormer: How neural networks are reshaping 3D rendering
| Yue Dong
RenderFormer, from Microsoft Research, is the first model to show that a neural network can learn a complete graphics rendering pipeline. It’s designed to support full-featured 3D rendering using only machine learning—no traditional graphics computation required.
In the news | NVIDIA Blog
Scene it to believe it: Populate 3D worlds quickly with NVIDIA AI blueprints
NVIDIA released the AI Blueprint for 3D object generation — a workflow that lets 3D artists create up to 20 3D objects to prototype a scene from a simple text prompt. Microsoft Research Asia’s TRELLIS NVIDIA NIM microservice works within…
Imagine a pair of smart glasses that detects its surroundings and speaks up at critical moments, such as when a car is approaching. That kind of split-second assistance could be transformative for people with low vision, but today’s visual AI…
Time-series data—measurements collected at regular intervals, like stock prices or traffic flows—has become a key driver of intelligent decision-making systems across industries. From medical monitoring to financial risk control, identifying patterns in this data is essential to many important operations.…
In the news | Microsoft Source Asia
Microsoft Research Asia launches Singapore lab to drive AI innovation, industrial transformation, and talent development
Xinxing Xu bridges AI research and real-world impact at Microsoft Research Asia – Singapore
Xinxing Xu is helping shape the work of Microsoft Research Asia – Singapore by turning advanced AI research into real-world solutions. Learn how he collaborates across sectors and disciplines to drive responsible innovation throughout Southeast Asia.
As large language models (LLMs) continue to improve at writing code, a key challenge has emerged: enabling them to generate complex, high-quality training data that actually reflects real-world programming. Currently, most data synthesis methods rely on simple code snippets as…