Morphable crowds

Ju, Eunjung; Choi, Myung Geol; Park, Minji; Lee, Jehee; Lee, Kang Hoon; Takahashi, Shin

doi:10.1145/1882262.1866162

Cited by 24 publications

(23 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several data driven synthesis methods target specific animated content, such as motion capture data [Kovar et al 2002;Pullen and Bregler 2002], crowds [Ju et al 2010;Li et al 2012], sequences of meshes [James et al 2007], and cloth wrinkles [Wang et al 2010;Kavan et al 2011]. While these techniques offer state-of-the-art results for the specific applications they address, our technique applies to a wider range of phenomena and provides more control to the user.…”

Section: Data-driven Animation Synthesismentioning

confidence: 99%

“…We solve the discrete optimization problem in Equation 13 using simulated annealing. For each motion path, we use the morphable model in [Ju et al 2010] to generate smooth temporal transitions between nodes, with blending weights determined by considering the path nodes as fourthorder NURBS data points.…”

Section: Graph Synthesismentioning

confidence: 99%

“…This case is inspired by prior motion-capture like scenarios (e.g. [Ju et al 2010]), where synthetic outputs are computed by data captured inputs. Here, the users only need to prepare polygonal model for the fish, with the spatial distributions and temporal motions extracted from the input.…”

Section: Captured Inputmentioning

confidence: 99%

“…as advecting particles [Zhu and Bridson 2005;Narain et al 2010], flocking herds [Narain et al 2009;Ju et al 2010;Sewall et al 2011], undulating threads [Wang et al 2009], and waving sheets [Wang et al 2010;Kavan et al 2011]. Due to their ubiquity and rich variety, such phenomena have long been important for many graphics effects.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic element textures

Wei

Lefèbvre

et al. 2013

ACM Trans. Graph.

View full text Add to dashboard Cite

(a) particles (b) herds (c) threads (d) sheets Figure 1: Dynamic element textures. Our method synthesizes a variety of repetitive spatial-temporal phenomena, such as particles, threads, and sheets, via a combination of constrained optimization and data driven computation. It offers control at both the coarse scale through spatial-temporal constraints (e.g. overall output shape and movement) and the fine scale through small input exemplars (bottom-left inlet of each image), which may be static (a) or animated (b), (c) and (d). Please refer to the accompanying video for corresponding animations of our results. AbstractMany natural phenomena consist of geometric elements with dynamic motions characterized by small scale repetitions over large scale structures, such as particles, herds, threads, and sheets. Due to their ubiquity, controlling the appearance and behavior of such phenomena is important for a variety of graphics applications. However, such control is often challenging; the repetitive elements are often too numerous for manual edit, while their overall structures are often too versatile for fully automatic computation.We propose a method that facilitates easy and intuitive controls at both scales: high-level structures through spatial-temporal output constraints (e.g. overall shape and motion of the output domain), and low-level details through small input exemplars (e.g. element arrangements and movements). These controls are suitable for manual specification, while the corresponding geometric and dynamic repetitions are suitable for automatic computation. Our system takes such user controls as inputs, and generates as outputs the corresponding repetitions satisfying the controls.Our method, which we call dynamic element textures, aims to produce such controllable repetitions through a combination of constrained optimization (satisfying controls) and data driven computation (synthesizing details). We use spatial-temporal samples as the core representation for dynamic geometric elements. We propose analysis algorithms for decomposing small scale repetitions from large scale themes, as well as synthesis algorithms for generating outputs satisfying user controls. Our method is general, producing a range of artistic effects that previously required disparate and specialized techniques.

show abstract

Section: Data-driven Animation Synthesismentioning

confidence: 99%

Section: Graph Synthesismentioning

confidence: 99%

Section: Captured Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic element textures

Wei

Lefèbvre

et al. 2013

ACM Trans. Graph.

View full text Add to dashboard Cite

show abstract

“…To address the issues above, various data-driven approaches have been proposed in the past few years. Some approaches aim to learn examples (mostly in terms of stateaction pairs) from video data which is then used to update the movement in particular situations instead of using behavior rules [7]- [12], while others try to calibrate the model parameters through automatic methods, so that the simulated behaviors can match the video data [13]- [17]. Existing works have shown the potential of data-driven approaches in crowd modeling, but they mainly focus on microscopic spatial crowd behaviors or motion update among existing examples.…”

Section: Introductionmentioning

confidence: 99%

A Generic Bi-Layer Data-Driven Crowd Behaviors Modeling Approach

Xing

Zhao

Zhang

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYCrowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance. key words: agent-based modeling, crowd simulation, data-driven approach, pedestrian behaviors

show abstract

A parsimonious model for locomotor in virtual agents based on dynamical coupling with the environment

Pino

Bedía

Serón

2016

Computer Animation & Virtual

View full text Add to dashboard Cite

In the domain of virtual agents modeling, computationalist approaches tend to predominate. The lack of plausibility of the rules in nature provides models with a low capacity to generalize and a very limited insight into behavioral phenomena. An alternative view is that behavior could emerge as a whole from the basic principles of non-linear dynamics that underlie such behavior in the environment. This paper presents a dynamic agent endowed with a sensing device, a controller and actuators to interact with the environment. The control architecture is based on ordinary differential equations with the function of modulating the stimulus signals to action signals under a sensory-motor flow. The parameter values are defined in an evolutionary process depending on the task to be performed. A series of experiments are presented to illustrate certain qualities of our model such as the adaptability to change, a highly intuitive and flexible design methodology, and a high degree of individual autonomy, among others. We think this kind of modeling is useful in the animation world for modeling flocks, crowds, and swarms as a valid alternative to other less parsimonious techniques. ). His areas of interest are mainly focused to the development of dynamical models of cognitive systems (bioinspired, ecological and synergetic approaches) including the development of embodied cognitive models, the convergence between dynamical systems and information theory, evolutionary and A-life techniques and the application of topological and differential geometry tools in the design of cognitive morphodynamical models. He was co-founder of the "Spanish

show abstract

Morphable crowds

Cited by 24 publications

References 0 publications

Dynamic element textures

Dynamic element textures

A Generic Bi-Layer Data-Driven Crowd Behaviors Modeling Approach

A parsimonious model for locomotor in virtual agents based on dynamical coupling with the environment

Contact Info

Product

Resources

About