nils-herrmann/clean_scirepeval huggingface dataset:
Sampling plausible solutions to multi-body constraint problems

Traditional collision intensive multi-body simulations are difficult to control due to extreme sensitivity to initial conditions or model parameters. Furthermore, there may be multiple ways to achieve any one goal, and it may be difficult to codify a user's preferences before they have seen the available solutions. In this paper we extend simulation models to include plausible sources of uncertainty, and then use a Markov chain Monte Carlo algorithm to sample multiple animations that satisfy constraints. A user can choose the animation they prefer, or applications can take direct advantage of the multiple solutions. Our technique is applicable when a probability can be attached to each animation, with “good” animations having high probability, and for such cases we provide a definition of physical plausibility for animations. We demonstrate our approach with examples of multi-body rigid-body simulations that satisfy constraints of various kinds, for each case presenting animations that are true to a physical model, are significantly different from each other, and yet still satisfy the constraints.
How Does CONDENSATION Behave with a Finite Number of Samples?

Condensation is a popular algorithm for sequential inference that resamples a sampled representation of the posterior. The algorithm is known to be asymptotically correct as the number of samples tends to infinity. However, the resampling phase involves a loss of information. The sequence of representations produced by the algorithm is a Markov chain, which is usually inhomogeneous. We show simple discrete examples where this chain is homogeneous and has absorbing states. In these examples, the representation moves to one of these states in time apparently linear in the number of samples and remains there. This phenomenon appears in the continuous case as well, where the algorithm tends to produce "clumpy" representations. In practice, this means that different runs of a tracker on the same data can give very different answers, while a particular run of the tracker will look stable. Furthermore, the state of the tracker can collapse to a single peak -- which has non-zero probability of being the wrong peak -- within time linear in the number of samples, and the tracker can appear to be following tight peaks in the posterior even in the absence of any meaningful measurement. This means that, if theoretical lower bounds on the number of samples are not available, experiments must be very carefully designed to avoid these effects.
Controllable and scalable simulation for animation

Simulation is an important means of generating animations. For example, we might use simulation to animate a virtual city in order to train emergency response personnel. Such an application requires the responsiveness and realism that simulation offers, and it also requires the ability to stage specific events in a very large animated environment. Traditional simulation technology fails on the latter count: it is difficult to direct a given simulation toward a desired outcome, and existing simulations rarely scale well to large virtual worlds. 
This thesis addresses controllable and scalable simulation for the purposes of computer animation. We describe a technique for directing the outcome of simulations by formulating the problem as one of probabilistic sampling. A Markov chain Monte Carlo (MCMC) algorithm is used to perform the sampling, which allows the generation of multiple animations from a desired distribution. Furthermore, if the distribution assigns probabilities according to the plausibility of an animation, then we can be certain that most of the sampled animations will appear reasonable to a viewer. A range of examples are presented from the domain of collision intensive rigid-body simulation, the majority of which could not be produced using previous technology. We also describe a new rigid-body simulation algorithm that was developed for this work. 
Scalable simulation is achieved through simulation culling, a method for focusing the computational effort on visible parts of a simulation. Aspects of the simulation that are not in view are not explicitly computed, thus saving large amounts of work. Approximations and random models are used to ensure that objects that leave the view re-enter when necessary in a plausible state, even though their full motion was not computed while out of view. A virtual fairground and a large virtual city are presented as case studies. These examples raise a number of open problems, which we discuss in some detail. 
This thesis treats control and scale as largely independent problems, yet we show that both may be viewed as sampling problems. We conclude with a look at how direction and culling might be integrated to enable large-scale virtual environments for realistic training and entertainment.
Bayesian structure from motion

Formulates structure from motion as a Bayesian inference problem and uses a Markov-chain Monte Carlo sampler to sample the posterior on this problem. This results in a method that can identify both small and large tracker errors and yields reconstructions that are stable in the presence of these errors. Furthermore, the method gives detailed information on the range of ambiguities in structure given a particular data set and requires no special geometric formulation to cope with degenerate situations. Motion segmentation is obtained by a layer of discrete variables associating a point with an object. We demonstrate a sampler that successfully samples an approximation to the marginal on this domain, producing a relatively unambiguous segmentation.
Shape, Contour and Grouping in Computer Vision

An Empirical-Statistical Agenda for  A Formal-Physical Agenda for Recognition.- Shape.- Shape Models and Object Recognition.- Order Structure, Correspondence, and Shape Based Categories.- Quasi-Invariant Parameterisations and Their Applications in Computer Vision.- Shading.- Representations for Recognition Under Variable Illumination.- Shadows, Shading, and Projective Ambiguity.- Grouping.- Grouping in the Normalized Cut Framework.- Geometric Grouping of Repeated Elements within Images.- Constrained Symmetry for Change Detection.- Grouping Based on Coupled Diffusion Maps.- Representation and  Integrating Geometric and Photometric Information for Image Retrieval.- Towards the Integration of Geometric and Appearance-Based Object  Recognizing Objects Using Color-Annotated Adjacency Graphs.- A Cooperating Strategy for Objects Recognition.- Statistics, Learning and Recognition.- Model Selection for Two View Geometry:A Review.- Finding Objects by Grouping Primitives.- Object Recognition with Gradient-Based Learning.
Finding people by sampling

We show how to use a sampling method to find sparsely clad people in static images. People are modeled as an assembly of nine cylindrical segments. Segments are found using an EM algorithm and then assembled into hypotheses incrementally, using a learned likelihood model. Each assembly step passes on a set of samples of its likelihood to the next; this yields effective pruning of the space of hypotheses. The collection of available nine-segment hypotheses is then represented by a set of equivalence classes, which yield an efficient pruning process. The posterior for the number of people is obtained from the class representatives. People are counted quite accurately in images of real scenes using a MAP estimate. We show the method allows top-down as well as bottom up reasoning. While the method can be overwhelmed by very large numbers of segments, we show that this problem can be avoided by quite simple pruning steps.
Visibility * techniques and applications Interactive ray tracing with the visibility complex

We describe a method of producing ray-traced images of 2D environments at interactive rates. The 2D environment consists of a set of disjoint, convex polygons. Our technique is based on the visibility complex [17,19] [Pocchiola M, Vegter G.  Visibility computations in 2D polygonal scenes. PhD thesis, Univ. Joseph Fourier, Grenoble I, France], a data structure in a dual space where a face of the visibility complex corresponds to a contiguous set of rays in the primary space with the same forward and backward views. Sweeping the viewing ray around a viewpoint corresponds to walking along a trajectory on the visibility complex. Producing a ray-traced image is equivalent to walking along and maintaining a set of trajectories. Generating ray-traced images with the visibility complex is very e$cient since it uses the coherence among the rays e!ectively. We have developed a new algorithm for the randomized incremental construction of the visibility complex. The advantage of using an incremental algorithm is that the history of the incremental construction yields an e$cient ray-query data structure, which is required for casting secondary rays. The performance of our algorithm is analyzed and a comparison is made with the classical ray-tracing algorithm.
Sampling, resampling and colour constancy

We formulate colour constancy as a problem of Bayesian inference, where one is trying to represent the posterior on possible interpretations given image data. We represent the posterior as a set of samples, drawn from that distribution using a Markov chain Monte Carlo method. We show how to build an efficient sampler. This approach has the advantage that it unifies the constraints on the problem, and represents possible ambiguities. In turn, a good description of possible ambiguities means that new information, instead of producing contradictions, is easily incorporated by resampling existing samples. The method is demonstrated on the case where surfaces seen in two distinct images are later discovered to be the same. We show examples using images of real scenes.
Computer Vision Tools for Finding Images and Video Sequences

Very large collections of images are now common. Indexing and searching such collections using indexing languages is difficult. Computer vision offers a variety of techniques for searching for pictures in large collections. Appearance methods compare images based on the overall content of the image using such criteria as similarity of color histograms, texture histograms, spatial layout, and filtered representations. Finding methods concentrate on matching subparts of images, defined in a variety of ways, in the hope of finding particular objects. These ideas are illustrated with a variety of examples from the current literature.
Dynamics Modeling and Culling

The tools described, permit including large numbers of complex dynamic models in a VRML world easily and efficiently while maintaining high frame rates. We describe three tools that together provide an environment for authoring cullable, dynamic, rigid-body objects in VRML and Java: a code transformation tool that exploits approximations to dynamical systems to enable culling; a runtime layer generator, which defines a simple standard interface between a VRML browser and dynamical systems described in Java; a rigid-body modeler, which allows users to interactively design the runtime layer and preview the dynamic behavior. The article describes these tools, including some example systems, and discusses the runtime performance improvements obtained. Our tools are applicable if the spatial range of the dynamic model can be bounded by a static volume, the model is closed to outside influence, the underlying equations are continuous, and the dimension (number of degrees of freedom) of the system is small. Note that while the article focuses on VRML and Java as the target environment, the underlying techniques apply to any rendering and language environment.
An Empirical-Statistical Agenda for Recognition

This piece first describes what I see as the significant weaknesses in current un- derstanding of object recognition. We lack good schemes for: using unreliable information — like radiometric measurements — effectively; integrating poten- tially contradictory cues; revising hypotheses in the presence of new information; determining potential representations from data; and suppressing individual dif- ferences to obtain abstract classes. The problems are difficult, but none are unapproachable, given a change of emphasis in our research.
Shape Representations from Shading Primitives

Diffuse interreflections mean that surface shading and shape are related in ways that are difficult to untangle; in particular, distant and invisible surfaces may affect the shading field that one sees. The effects of distant surfaces are confined to relatively low spatial frequencies in the shading field, meaning that we can expect signatures, called shading primitives, corresponding to shape properties. We demonstrate how these primitives can be used to support the construction of useful shape representations. Approaches to this include testing hypotheses of geometric primitives for consistency with the shading field, and looking for shading events that are distinctive of some shape event. We show that these approaches can be composed, leading to an attractive process of representation that is intrinsically bottom up. This representation can be extracted from images of real scenes, and that the representation is diagnostic.
Learning to Find Pictures of People

Finding articulated objects, like people, in pictures presents a particularly difficult object recognition problem. We show how to find people by finding putative body segments, and then constructing assemblies of those segments that are consistent with the constraints on the appearance of a person that result from kinematic properties. Since a reasonable model of a person requires at least nine segments, it is not possible to present every group to a classifier. Instead, the search can be pruned by using projected versions of a classifier that accepts groups corresponding to people. We describe an efficient projection algorithm for one popular classifier, and demonstrate that our approach can be used to determine whether images of real scenes contain people.
Finding objects by grouping primitives

We describe the use of a representation, called a body plan, to segment and to recognize people and animals in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships, between parts. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand; the other involves determining whether a picture contains a horse, using a body plan learned from image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets. The mechanism of recognition by assembly is very general; we describe previous work on finding clothing by marking folds and then assembling groups of folds.
Shading primitives: finding folds and shallow grooves

Diffuse interreflections cause effects that make current theories of shape from shading unsatisfactory. We show that distant radiating surfaces produce radiosity effects at low spatial frequencies. This means that, if a shading pattern has a small region of support, unseen surfaces in the environment can only produce effects that vary slowly over the support region. It is therefore relatively easy to construct matching processes for such patterns that are robust to interreflections. We call regions with these patterns "shading primitives". Folds and grooves on surfaces provide two examples of shading primitives; the shading pattern is relatively independent of surface shape at a fold or a groove, and the pattern is localised. We show that the pattern of shading can be predicted accurately by a simple model, and derive a matching process from this model. Both groove and fold matchers are shown to work well on images of real scenes.
To appear in the 1998 Symposium on the Virtual Reality Modeling Language , Monterey , CA , Feb 16-19 1998 E cient Dynamics Modeling for VRML and JavaStephen Chenney

Using dynamical systems rather than keyframing to animate a world is a desirable yet computationally expensive approach. We present techniques for culling dynamical systems that avoid unnecessary computation, and describe tools for automating much of the required work. Based on qualitative observations of how viewer's predict dynamical state over time, we identify methods for generating state while ensuring consistency, which we deene as ensuring that a viewer's predictions are satissed. Our tools take as input a description of a dynamical system, and produce an alternate description that may be eeciently culled. We also describe an interactive modeler in which authors attach dynamic variables to geometric transformations, allowing the modular re-use of dynamical systems. Together, our tools enable large numbers of complex dynamic models to be eeciently and easily included in a VRML world while maintaining high frame rates.
Computer Vision for Human–Machine Interaction: Probabilistic Models of Verbal and Body Gestures

This chapter describes several probabilistic techniques for representing, recognizing, and generating spatiotemporal connguration sequences. We rst describe how such techniques can be used to visually track and recognize lip movements to augment a speech recognition system. We then demonstrate additional techniques that can be used to animate video footage of talking faces and synchronize it to diierent sentences of an audio track. Finally we outline alternative low-level representations that are needed to apply these techniques to articulated body gestures.  While uttering a sentence we express very ne grained verbal gestures as complex lip conngurations over time, and while performing body actions, we generate articulated connguration sequences of jointed arm and leg segments. Such conngurations lie in constrained subspaces and diierent y Part of this report is published at ICCV'95 9]
The appearance of clothingJohn

Loose clothing has a complicated shape, due to the folds that are generated over its surface. In many cases, these patterns of folds can be predicted from buckling theory. Previous work has shown that the shading pattern of these individual folds can be learned and marked in an image using techniques that are robust to the eeects of diiuse interreeections. In this paper, we demonstrate a program that groups image folds according to the predictions of a linearized buckling model of cloth. Folds in cloth are roughly straight, roughly parallel, and roughly evenly spaced. These geometric properties inform a grouping process that can segment an image and produce segments that are likely to correspond to human clothing. The method is demonstrated on images of real scenes.
Efficient dynamics modeling for VRML and Java

Using dynamical systems rather than keyframing to animate a world is a desirable yet computationally expensive approach. We present techniques for culling dynamical systems that avoid unnecessary computation, and describe tools for automating much of the required work. Based on qualitative observations of how viewer’s predict dynamical state over time, we identify methods for generating state while ensuring consistency, which we define as ensuring that a viewer’s predictions are satisfied. Our tools take as input a description of a dynamical system, and produce an alternate description that may be efficiently culled. We also describe an interactive modeler in which authors attach dynamic variables to geometric transformations, allowing the modular re-use of dynamical systems. Together, our tools enable large numbers of complex dynamic models to be efficiently and easily included in a VRML world while maintaining high frame rates. CR Descriptors: I.3.7 [Computer Graphics]: ThreeDimensional Graphics and Realism Virtual reality; I.6.5 [Simulation and Modeling]: Model Development Modeling methodologies I.6.8 [Simulation and Modeling]: Types of Simulation Animation
View-dependent culling of dynamic systems in virtual environments

Culling of Dynamic Systems in Virtual Environments Stephen Chenney David Forsyth* University of California at Berkeley Scalable rendering of virtual environments requires culling objects that have no etTect on the view. This paper explores culling moving objects by not solving the equations of motion of objects that don’t tiect the view. While this approach could be scalable for many kinds of environments, it raises two problems: consistency ensuring that objects that come back into view do so in the right state and completeness ensuring that objects that would have entered the view volume as a result of their motions, do so. Solutions to these problems lie in studying the statistics of the motion of objects. We show strategies for addressing the problem of consistency, with a number of examples that illustrate both the difficulties involved in, and the potential gains to be obtained by, formulating a comprehensive approach. CR Descriptors: 1.3.7 [Computer Graphics]: ThreeDimensional Graphi@ and Realism Virtual reality 1.6.5 [Simulation and Modeling]: Model Development Modeling methodologies 1.6.8 [Simulation and Modeling]: Types of Simulation Animation
Finding people and animals by guided assembly

This paper describes a new representation for people and animals, called a body plan. The representation is an organized collection of grouping hints obtained from constraints on color, texture, shape, and geometrical relations. Body plans can be learned from image data, using established statistical learning techniques. Body plans are well adapted to segmentation and recognition in complex environments, such as the huge libraries of digitized images now becoming widely available. Two specific applications of body plans are presented: an algorithm that determines whether an image depicts a scantily clad human and an algorithm that learns and uses a body plan to find pictures of horses. Both algorithms demonstrate excellent performance on large, poorly controlled input data.
Body plans

This paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. Body plans can be learned from image data, using established statistical learning techniques. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand; the other involves determining whether a picture contains a horse, using a body plan learned from image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.