92 research outputs found

    Transforming Dress

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    In this paper, we produce a fashion show in which a dress is transformed over time, with a storyline of a robot that experiences some emotional changes after falling in love, sheds a symbolic teardrop and at the end becomes a lovely woman. This transformability, which cannot be done in a real fashion show, could open the potential for a new kind of creativity in the fashion industry

    Parallelized Incomplete Poisson Preconditioner in Cloth Simulation

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    Efficient cloth simulation is an important problem for interactive applications that involve virtual humans, such as computer games. A common aspect of many methods that have been developed to simulate cloth is a linear system of equations, which is commonly solved using conjugate gradient or multi-grid approaches. In this paper, we introduce to the computer gaming community a recently proposed preconditioner, the incomplete Poisson preconditioner, for conjugate gradient solvers. We show that the parallelized incomplete Poisson preconditioner (PIPP) performs as well as the current state-of-the-art preconditioners, while being much more amenable to standard thread-level parallelism. We demonstrate our results on an 8-core Apple* Mac* Pro and a 32-core code name Emerald Ridge system

    Efficient Clothing Fitting from Data.

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    A major drawback of shopping for clothes on-line is that the customer cannot try on clothes and see if they fit or suit them. One solution is to display clothing on an avatar, a 3D graphical model of the customer. However the normal technique for modeling clothing in computer graphics, cloth dynamics, suffers from being too processor intensive and is not practical for real time applications. Hence, retailers normally rely on a fixed set of body models to which clothes are pre-fitted. As the customer has to choose from this limited set the fit is typicallly not very representative of how the real clothes will fit. We propose a method that uses a compromise between these two methods. We generate a set of example avatars by performing Principal Component Analysis on a dataset of avatars. Clothes are pre-fitted to these examples off-line. Instead of asking the customer to choose from the set of examples we are able to represent the users avatar as a weighted sum of the examples, we then fit clothes as the same weighted sum over the clothes fitted to the examples

    ๋Œ€์นญ์ ์ธ ์˜์ƒ์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฐ€์†์„ ์œ„ํ•œ ํŒจํ„ด ๋ฏธ๋Ÿฌ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2019. 2. ๊ณ ํ˜•์„.๋ณธ ๋…ผ๋ฌธ์€ ์˜์ƒ-๋ฐ”๋”” ์‹œ๋ฎฌ๋ ˆ์ด์…˜์˜ ์†๋„ ํ–ฅ์ƒ์„ ์œ„ํ•œ ํŒจํ„ด๋ฏธ๋Ÿฌ๋ง ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์€ ๋ชธ ๋งค์‰ฌ์™€ ์˜ท์˜ ํŒจ๋„์ด ์œ„์น˜ํ•œ Y-Zํ‰๋ฉด์— ๋Œ€ํ•ด ๋Œ€์นญ์ผ ๊ฒฝ์šฐ์— ์‚ฌ์šฉ๊ฐ€๋Šฅํ•˜๋‹ค. ๋ณดํ†ต์˜ ๋‚จ์„ฑ๋ณต์ด๋‚˜ ๊ธฐ์„ฑ๋ณต๊ณผ ๊ฐ™์€ ์˜ท์ด ์ขŒ์šฐ๊ฐ€ ๋Œ€์นญ์ธ ๊ฒฝ์šฐ๊ฐ€ ๋งŽ๋‹ค. ๊ธฐ์กด ์‹œ๋ฎฌ๋ ˆ์ด์…˜์—์„œ๋Š” ๋ชจ๋“  ์˜ท์˜ ์ •์ ๋“ค์— ๋Œ€ํ•ด conjugate gradient ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•ด ์‹œ์Šคํ…œ ํ–‰๋ ฌ์„ ํ’€์—ˆ๋‹ค. ๋ฌธ์ œ๋Š” conjugate gradient ๋ฐฉ๋ฒ•์€ ์ •์  ์ˆ˜์— ๋Œ€ํ•ด ์ง€์ˆ˜์ ์ธ ์‹œ๊ฐ„ ๋ณต์žก๋„๋ฅผ ๊ฐ€์ง€๋ฏ€๋กœ, ๊ณ ํ•ด์ƒ๋„๋ฅผ ์œ„ํ•ด ์ •์ ์˜ ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ• ์ˆ˜๋ก ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๊ฐ„์ด ์ง€์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. Pattern Mirroring ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜๋ฉด ๊ณ„์‚ฐํ•ด์•ผํ•˜๋Š” ์‹œ์Šคํ…œ ๋ฐฉ์ •์‹์˜ ์–‘์ด ๋ฐ˜์ ˆ๋กœ ์ค„์–ด๋“ค๊ธฐ ๋•Œ๋ฌธ์—, ์‹œ๋ฎฌ๋ ˆ์ด์…˜์— ํ•„์š”ํ•œ ์‹œ๊ฐ„๋„ ์ค„์–ด๋“ค๊ฑฐ๋ผ๊ณ  ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ํŒจํ„ด๋ฏธ๋Ÿฌ๋ง ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜๋ฉด 1.4๋ฐฐ (37%)์˜ ์†๋„ ํ–ฅ์ƒ์„ ๋ณด์˜€๋‹ค. 1์žฅ ๋„์ž…์—์„œ๋Š” ์˜ท์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ํ•˜๋Š” ๊ณผ์ •์ธ ์‹œ์Šคํ…œ ๋ฐฉ์ •์‹์„ ํ‘ธ๋Š” ๋ฐฉ๋ฒ•, ์ถฉ๋Œ์ฒ˜๋ฆฌ๋ฅผ ํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ๋‹ค. iterative method์ธ conjugate gradient๊ฐ€ ์˜ท์˜ ์ •์ ๋“ค์˜ ์†๋„๋ฅผ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. 2์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ฐ€์†ํ™”๋ฅผ ์œ„ํ•œ ์—ฐ๊ตฌ๋ฅผ ์†Œ๊ฐœํ•œ๋‹ค. 3์žฅ์—์„œ pattern mirroring ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์†Œ๊ฐœํ•œ๋‹ค. 4์žฅ์—์„œ๋Š” ํŒจํ„ด ๋ฏธ๋Ÿฌ๋ง ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค๋ฉด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ๊ฐ€ ๋ช‡๊ฐ€์ง€ ์žˆ๋Š”๋ฐ, ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ๋ฐฉ๋ฒ•์— ๋Œ€ํ•ด ์„ค๋ช…ํ•œ๋‹ค. 5์žฅ์—์„œ๋Š” ํŒจํ„ด ๋ฏธ๋Ÿฌ๋ง ๋ฐฉ๋ฒ•์„ ๊ธฐ์กด์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ•ด์„œ ์†๋„ ํ–ฅ์ƒ์„ ๋„ํ‘œ๋กœ ์ œ์‹œํ•˜๊ณ , ๊ฒฐ๊ณผ ์ด๋ฏธ์ง€๋ฅผ ๋น„๊ตํ•œ๋‹ค.This paper describes the Pattern mirroring algorithm to reduce simulation time for cloth body simulation. This method is applicable for symmetric panel and symmetric body meshes centered on YZ plane: typically, man's suit and ready-make cloth is target of this method. As the ordinal simulation method, apply conjugate gradient method to every vertices on cloth mesh in order to solve system matrix. The problem is that the time for simulation is getting longer as the number of cloth vertices increases for high resolution. This is because the time complexity of conjugate gradient is exponential. Using pattern mirroring method, size of system matrix equation is half comparing ordinal method. So I can expect that the time for simulation reduces. The proposed method reduces simulation time up to 1.4 times (37%), by halving the matrix size of the linear equation. At chapter 1 introduction, describe the process of simulation, method of solving system equation and collision handling. An iterative method 'conjugate gradient method' is used to determine velocity of vertices of clothes. At chapter 2 relative work, explain about previous acceleration research for cloth simulation. At chapter 3, explain Pattern mirroring algorithm. But some problems could occur when using this method. At chapter 4, suggest solutions to handle these artifact as post-process step. At chapter 5, represent table to comparing the average time to simulate cloth in ordinal method and pattern mirroring method. Also represent image to difference of two result. Finally at chapter 6, describe conclusion and limitation of Pattern mirroring algorithm.Abstract Contents List of Figures List of Tables 1 Introduction 1.1 Time integration method 1.2 System matrix 1.3 Conjugate gradient method 1.4 Collision handling method 1.5 Overview of Pattern mirroring algorithm 2 Previous Work 3 Pattern Mirroring Method 3.1 1st step: Set Constraint Plane and Halving Mesh 3.2 2nd step: Simulation for Half Pane 3.3 3rd step: Mirroring Half Mesh 4 Artifacts Handling 4.1 Project crossed vertices at halving step 4.2 Penetration between original and mirrored mesh 5 Experiment Result 5.1 T-shirt 5.2 Jacket 6 Conclusion Bibliography ์ดˆ๋ก ๊ฐ์‚ฌ์˜๊ธ€Maste

    Fast Simulation of Mass-Spring Systems

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    We describe a scheme for time integration of mass-spring systems that makes use of a solver based on block coordinate descent. This scheme provides a fast solution for classical linear (Hookean) springs. We express the widely used implicit Euler method as an energy minimization problem and introduce spring directions as auxiliary unknown variables. The system is globally linear in the node positions, and the non-linear terms involving the directions are strictly local. Because the global linear system does not depend on run-time state, the matrix can be pre-factored, allowing for very fast iterations. Our method converges to the same final result as would be obtained by solving the standard form of implicit Euler using Newtonโ€™s method. Although the asymptotic convergence of Newtonโ€™s method is faster than ours, the initial ratio of work to error reduction with our method is much faster than Newtonโ€™s. For real-time visual applications, where speed and stability are more important than precision, we obtain visually acceptable results at a total cost per timestep that is only a fraction of that required for a single Newton iteration. When higher accuracy is required, our algorithm can be used to compute a good starting point for subsequent Newtonโ€™s iteration

    Automatic modeling of virtual humans and body clothing

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    Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewe
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