D. Thalmann

Publications
Influence

Joint-dependent local deformations for hand animation and object grasping

N. Magnenat-Thalmann, R. Laperrière, D. Thalmann
Computer Science
1 December 1989

Algorithms and methods are presented for animating the hands of a synthetic actor. Expand

Hierarchical Model for Real Time Simulation of Virtual Human Crowds

S. Musse, D. Thalmann
Computer Science
IEEE Trans. Vis. Comput. Graph.
1 April 2001

This paper presents ViCrowd, a behavioral-based multi-level framework to simulate virtual human crowds in real time. Expand

3D Convolutional Neural Networks for Efficient and Robust Hand Pose Estimation from Single Depth Images

Liuhao Ge, H. Liang, Junsong Yuan, D. Thalmann
Computer Science
IEEE Conference on Computer Vision and Pattern…
21 July 2017

We propose a simple, yet effective approach for real-time hand pose estimation from single depth images using three-dimensional Convolutional Neural Networks (CNNs). Expand

Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs

Liuhao Ge, H. Liang, Junsong Yuan, D. Thalmann
Computer Science
IEEE Conference on Computer Vision and Pattern…
23 June 2016

We propose a novel 3D regression method using multi-view CNNs that can better exploit depth cues to recover fully 3D information of hand joints without model fitting. Expand

A global human walking model with real-time kinematic personification

R. Boulic, N. Magnenat-Thalmann, D. Thalmann
Computer Science
The Visual Computer
1 November 1990

The experimental context corresponding to the model is extended by allowing a continuous variation of global spatial and temporal parameters according to the motion rendition expected by the animator. Expand

Autonomy

D. Thalmann
Computer Science
SIGGRAPH Courses
31 July 2005

“An autonomous agent is a system situated within and a part of an environment that senses that environment and acts on it, over time, in pursuit of its own agenda and so as to effect what it senses in the future.” “Humans and some animals are at the high end of being an agent, with multiple, conflicting drives, multiples senses, multiple possible actions, and complex sophisticated control structures. Expand

ETAF: An extended trust antecedents framework for trust prediction

G. Guo, J. Zhang, D. Thalmann, N. Yorke-Smith
Computer Science
IEEE/ACM International Conference on Advances in…
17 August 2014

We propose a principled approach that predicts implicit trust from users' interactions, by extending a well-known trust antecedents framework. Expand

Stepping into virtual reality

Mario Arturo Gutierrez Alonso, F. Vexo, D. Thalmann
Computer Science, Engineering
4 April 2008

Virtual reality techniques are increasingly becoming indispensible in many areas including medicine, entertainment, architecture, education and manufacturing, with VR tools being used for testing and prototyping products at design stages, as well as for creating applications in finished products. Expand

A Model of Human Crowd Behavior : Group Inter-Relationship and Collision Detection Analysis

S. Musse, D. Thalmann
Sociology, Computer Science
Computer Animation and Simulation
1997

This paper presents a model of crowd behavior to simulate the motion of a generic population in a specific environment, and a simple visit to a museum. Expand

Merging trust in collaborative filtering to alleviate data sparsity and cold start

G. Guo, J. Zhang, D. Thalmann
Computer Science
Knowl. Based Syst.
1 February 2014

We propose a novel trust-based approach called ''Merge'' to incorporate social trust information (i.e., trusted neighbors explicitly specified by users) in providing recommendations. Expand

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