Daniel Thalmann

Bio: Daniel Thalmann is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Computer animation & Animation. The author has an hindex of 75, co-authored 672 publications receiving 21114 citations. Previous affiliations of Daniel Thalmann include École Normale Supérieure & ETH Zurich.

A global human walking model with real-time kinematic personification

Abstract: Presents a human walking model built from experimental data based on a wide range of normalized velocities. The model is structured on two levels. On the first level, global spatial and temporal characteristics are generated. On the second level, a set of parameterized trajectories produce both the position of the body in space and the internal body configuration. This is performed for a standard structure and an average configuration of the human body. 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. The model is based on a simple kinematic approach designed to keep the intrinsic dynamic characteristics of the experimental model. Such an approach also allows a personification of the walking action in an interactive real-time context in most cases. A correction automata of such motion is then proposed

Hierarchical model for real time simulation of virtual human crowds

Abstract: We describe a model for simulating crowds of humans in real time. We deal with a hierarchy composed of virtual crowds, groups, and individuals. The groups are the most complex structure that can be controlled in different degrees of autonomy. This autonomy refers to the extent to which the virtual agents are independent of user intervention and also the amount of information needed to simulate crowds. Thus, depending on the complexity of the simulation, simple behaviors can be sufficient to simulate crowds. Otherwise, more complicated behavioral rules can be necessary and, in this case, it can be included in the simulation data in order to improve the realism of the animation. We present three different ways for controlling crowd behaviors: by using innate and scripted behaviors; by defining behavioral rules, using events and reactions; and by providing an external control to guide crowd behaviors in real time. The two main contributions of our approach are: the possibility of increasing the complexity of group/agent behaviors according to the problem to be simulated and the hierarchical structure based on groups to compose a crowd.

Simulation of Facial Muscle Actions Based on Rational Free Form Deformations

Abstract: This paper describes interactive facilities for simulating abstract muscle actions using rational free form deformations (RFFD). The particular muscle action is simulated as the displacement of the control points of the control-unit for an RFFD defined on a region of interest. One or several simulated muscle actions constitute a minimum perceptible action (MPA), which is defined as the atomic action unit, similar to action unit (AU) of the facial action coding system (FACS), to build an expression

Dressing animated synthetic actors with complex deformable clothes

Abstract: Discusses the use of physics-based models for animating clothes on synthetic actors in motion. In this approach, cloth pieces are first designed with polygonal panels in two dimensions, and are then seamed and attached to the actor's body in three dimensions. After the clothes are created, physical properties are simulated and then clothes are animated according to the actor's motion in a physical environment. The paper describes the physical models used and then addresses several problems encountered. It examines how to constrain the elements of deformable objects which are either seamed together or attached to rigid moving objects. It also describes a new approach to the problem of handling collisions among the cloth elements themselves, or between a cloth element and a rigid object like the human body. Finally, the paper discusses how to reduce the number of parameters for improving the interface between the animator and the physics-based model

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

Abstract: This paper presents a model of crowd behavior to simulate the motion of a generic population in a specific environment. The individual parameters are created by a distributed random behavioral model which is determined by few parameters. This paper explores an approach based on the relationship between the autonomous virtual humans of a crowd and the emergent behavior originated from it. We have used some concepts from sociology to represent some specific behaviors and represent the visual output. We applied our model in two applications: a graphic called sociogram that visualizes our population during the simulation, and a simple visit to a museum. In addition, we discuss some aspects about human crowd collision.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These