Computational Models for Multiparty Turn-Taking - sample2-segment-scene
Computational Models for Multiparty Turn-Taking - sample2-segment-scene See more on this video at
72
Facilitating Multiparty Dialog with Gaze, Gesture, and Speech - sample1-segment-overhead
Facilitating Multiparty Dialog with Gaze, Gesture, and Speech - sample1-segment-overhead See more on this video at
91
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample1-full-scene
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample1-full-scene See more on this video at
62
Facilitating Multiparty Dialog with Gaze, Gesture, and Speech - sample1-segment-scene
Facilitating Multiparty Dialog with Gaze, Gesture, and Speech - sample1-segment-scene See more on this video at
87
Dialog in the Open World: Platform and Applications - ReceptionistICMI
Dialog in the Open World: Platform and Applications - ReceptionistICMI See more on this video at
121
Natural Communication about Uncertainties in Situated Interaction
Natural Communication about Uncertainties in Situated Interaction - Demo See more on this video at
299
Deus ex Machina
See more on this video at microsoft.com/en-us/research/video/deus-ex-machina
984
65.2
Computational Models for Multiparty Turn-Taking - sample2-segment-overhead
Computational Models for Multiparty Turn-Taking - sample2-segment-overhead See more on this video at
59
Computational Models for Multiparty Turn-Taking - sample1-full-overhead
Computational Models for Multiparty Turn-Taking - sample1-full-overhead See more on this video at
56
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample2-full-overhead
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample2-full-overhead See more on this video at
54
Dialog in the Open World: Platform and Applications - multiparty_learning_game
Dialog in the Open World: Platform and Applications - multiparty_learning_game See more on this video at
100
Specializing Shaders
See more on this video at microsoft.com/en-us/research/video/specializing-shaders-2
88
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample2-full-scene
Multiparty Turn Taking in Situated Dialog: Study, Lessons, and Directions - sample2-full-scene See more on this video at
60
Community Detection in the Stochastic Block Model: Approximate Belief Propagation
The stochastic block model is one of the simplest models for a random graph with different types of vertices, known as communities.
1 599
177.3
Towards Practical Machine Learning with Differential Privacy and Variants
Machine learning (ML) has become one of the most powerful classes of tools for artificial intelligence, personalized web services and data science problems across fields.
976
32.2
CABI 2016 Session 2
See more on this video at microsoft.com/en-us/research/video/cabi-2016-session-2
80
Symbolic Lagrangian Mechanics
See more on this video at microsoft.com/en-us/research/video/symbolic-lagrangian-mechanics
690
15
Making Faces
Making Faces video for SIGGRAPH 98 See more on this video at microsoft.com/en-us/research/video/making-faces-3
311
20.4
Manipulating Motion Capture Data
See more on this video at microsoft.com/en-us/research/video/manipulating-motion-capture-data
841
93.1
Proceedural Geometry for Real Time Graphics
See more on this video at microsoft.com/en-us/research/video/proceedural-geometry-real-time-graphics
208
Improving procedural textures using HLSL's symbolic differentiation
See more on this video at microsoft.com/en-us/research/video/improving-procedural-textures-using-hlsls-symbolic-differentiation
237
Embracing Uncertainty exhibit
Indepth look at the Embracing Uncertainty exhibit on probability theory at the 2010 Royal Society Summer Science Exhibition.
134
WEye Program: Improving Communication Between Pair Programmers Using Shared Gaze Awareness
This video presents our research improving remote pair programming using a eyetracking-enabled, shared gaze visualization employed in Visual Studio.
689
32.4
Micro-Baseline Stereo
Tradeoffs exist between the baseline or distance between cameras and the difficulty of matching corresponding points in stereo and structure from motion.
666
31.3
Modeling and Rendering of Heterogeneous Translucent Materials Using the Diffusion Equation
In this paper, we propose techniques for modeling and rendering of heterogeneous translucent materials that enable acquisition from measured samples, interactive editing of material attributes, and
403
22.1
Reflectance Scanning: Estimating Shading Frame and BRDF with Generalized Linear Light Sources
We present a generalized linear light source solution to estimate both the local shading frame and anisotropic surface reflectance of a planar spatially varying material sample.
1 637
23.4
Image Based Relighting Using Neural Networks
We present a neural network regression method for relighting realworld scenes from a small number of images.
2 985
39.4
Manifold Bootstrapping for SVBRDF Capture
Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms a low-dimensional
496
27.2
How interns at our New England Lab impact research at Microsoft
Microsoft Research leaders and interns comment on the contributions interns make and the opportunities they have to work with some of the world's top researchers.
1 786
148.4
How interns at our New York Lab impact research at Microsoft
Microsoft Research leaders and interns comment on the contributions interns make and the opportunities they have to work with some of the world's top researchers.
4 023
57.9