Piles of objects

Hsu, Shu-Wei; Keyser, John

doi:10.1145/1866158.1866181

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…As discussed in [Cho et al 2007], physically implausible configurations are often desired in real production scenarios. Figure 13 demonstrates potential cases which cannot be produced by physical simulation (not stable) even with the assistance of frozen elements during piling [Hsu and Keyser 2010]. We have also found such complex elements very difficult to synthesize well with only one sample per element as in our basic algorithm (see Figure 14) and prior data-driven element synthesis methods which, to our knowledge, predominantly use a single sample per element.…”

Section: Boundary Handlingmentioning

confidence: 94%

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Discrete element textures

Wei

Tong

2011

ACM SIGGRAPH 2011 Papers

121

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(a) plum stack (b) pebble sculpture (c) dish of corn kernels, carrots, beans (d) spaghetti Figure 1: Discrete element textures. Given a small input exemplar (left within each image), our method synthesizes a corresponding output with user specified coarse-scale domain (right within each image). Using a data driven approach, we can achieve a variety of effects, including (a) regular distribution, (b) output with different domain shape and boundary conditions from the input, (c) mixture of different elements, and (d) deformable and elongated shapes. AbstractA variety of phenomena can be characterized by repetitive small scale elements within a large scale domain. Examples include a stack of fresh produce, a plate of spaghetti, or a mosaic pattern.Although certain results can be produced via manual placement or procedural/physical simulation, these methods can be labor intensive, difficult to control, or limited to specific phenomena.We present discrete element textures, a data-driven method for synthesizing repetitive elements according to a small input exemplar and a large output domain. Our method preserves both individual element properties and their aggregate distributions. It is also general and applicable to a variety of phenomena, including different dimensionalities, different element properties and distributions, and different effects including both artistic and physically realistic ones. We represent each element by one or multiple samples whose positions encode relevant element attributes including position, size, shape, and orientation. We propose a sample-based neighborhood similarity metric and an energy optimization solver to synthesize desired outputs that observe not only input exemplars and output domains but also optional constraints such as physics, orientation fields, and boundary conditions. As a further benefit, our method can also be applied for editing existing element distributions.

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Section: Boundary Handlingmentioning

confidence: 94%

“…On one hand this provides the flexibility for the users to choose whatever shapes they like, but on the other hand it may be a nuisance if the users do not feel like doing so. For the latter case it would be interesting to apply more automatic methods to determine the output shape [Hsu and Keyser 2010].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Discrete element textures

Wei

Tong

2011

ACM SIGGRAPH 2011 Papers

121

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“…Rather than uniformly modifying stages in simulating each object, [Hsu and Keyser 2010] improves the overall performance by stopping simulation of objects that satisfy certain conditions. Speed-ups achieved in this paper range from 1.2 to 6.0 times depending on specific scenarios and various other factors.…”

Section: M3mentioning

confidence: 99%

“…In our work, we generate large scale animations of rigid bodies using algorithms in [Hsu and Keyser 2009] and [Hsu and Keyser 2010].…”

Section: Introductionmentioning

confidence: 99%

Believability in simplifications of large scale physically based simulation

Han¹,

Hsu²,

McNamara³

et al. 2013

Proceedings of the ACM Symposium on Applied Perception

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Figure 1: Screenshots from two clips used in our experiment. Screenshots in upper row are from clip simulated using classic impulse based method, while screenshots in lower row are from clip simulated with statistical simulation method [Hsu and Keyser 2009]. We verify that certain hypotheses that are often just assumed to be true do in fact hold, and therefore that the simplified simulation is an acceptable substitute. AbstractWe verify two hypotheses which are assumed to be true only intuitively in many rigid body simulations. I: In large scale rigid body simulation, viewers may not be able to perceive distortion incurred by an approximated simulation method. II: Fixing objects under a pile of objects does not affect the visual plausibility. Visual plausibility of scenarios simulated with these hypotheses assumed true are measured using subjective rating from viewers. As expected, analysis of results supports the truthfulness of the hypotheses under certain simulation environments. However, our analysis discovered four factors which may affect the authenticity of these hypotheses: number of collisions simulated simultaneously, homogeneity of colliding object pairs, distance from scene under simulation to camera position, and simulation method used.We also try to find an objective metric of visual plausibility from eye-tracking data collected from viewers. Analysis of these results indicates that eye-tracking does not present a suitable proxy for measuring plausibility or distinguishing between types of simulations.

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“…Fearing[2000] took the concept of angle of repose to generate realistic snow piles. Hsu and Keyser[2010] also use this concept to model piles of rigid objects in simulation, but the shapes are limited to realistic conelike shapes. These methods are designed to generate static models or offer only limited control over shapes.…”

Section: Directable Simulation Controlmentioning

confidence: 99%

Automated constraint placement to maintain pile shape

Hsu¹,

Keyser²

2012

ACM Trans. Graph.

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Figure 1: To create a pile of a given shape, a user specifies a desired shape and starting configuration of objects. At right, a pile starting in a desired configuration will collapse under simulation. The middle shows the result of adding constraints judiciously to maintain the pile shape. At right is the desired control mesh (in white), with the objects that have had constraints applied colored in cyan. AbstractWe present a simulation control to support art-directable stacking designs by automatically adding constraints to stabilize the stacking structure. We begin by adapting equilibrium analysis in a local scheme to find "stable" objects of the stacking structure. Next, for stabilizing the structure, we pick suitable objects from those passing the equilibrium analysis and then restrict their DOFs by managing the insertion of constraints on them. The method is suitable for controlling stacking behavior of large scale. Results show that our control method can be used in varied ways for creating plausible animation. In addition, the method can be easily implemented as a plug-in into existing simulation solvers without changing the fundamental operations of the solvers.

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Piles of objects

Cited by 6 publications

References 19 publications

Discrete element textures

Discrete element textures

Believability in simplifications of large scale physically based simulation

Automated constraint placement to maintain pile shape

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