Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games最新文献

VR Natural Walking in Impossible Spaces VR自然行走在不可能的空间

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488305

Daniel Lochner, J. Gain

{"title":"VR Natural Walking in Impossible Spaces","authors":"Daniel Lochner, J. Gain","doi":"10.1145/3487983.3488305","DOIUrl":"https://doi.org/10.1145/3487983.3488305","url":null,"abstract":"Locomotion techniques in Virtual Reality (VR) are the means by which users traverse a Virtual Environment (VE) and are considered an integral and indispensable part of user interaction. This paper investigates the potential that natural walking in impossible spaces provides as a viable locomotion technique in VR when compared to conventional alternatives, such as teleportation, arm-swinging and touchpad/joystick. In this context, impossible spaces are locally Euclidean orbit-manifolds — subspaces separated by portals that are individually consistent but are able to impossibly overlap in space without interacting. A quantitative user experiment was conducted with n = 25 participants, who were asked to complete a set of tasks inside four houses, in each case using a different locomotion technique to navigate. After completing all tasks for a given house, participants were then asked to complete a set of three questionnaires regarding the technique used, namely the Simulator Sickness Questionnaire (SSQ), Game Experience Questionnaire (GEQ) and System Usability Scale (SUS). Time for task completion was also recorded. It was found that natural walking in impossible spaces significantly improves (α = 0.05) immersion (as compared to teleportation and touchpad/joystick, r > 0.7) and system usability (over touchpad/joystick and arm-swinging, r ≥ 0.38), but seems to lead to slower task completion.","PeriodicalId":170509,"journal":{"name":"Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Catching and Throwing Control of a Physically Simulated Hand 物理模拟手的接球和投掷控制

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488300

Yunhao Luo, Kaixiang Xie, S. Andrews, P. Kry

引用次数: 3

QLB: Collision-Aware Quasi-Newton Solver with Cholesky and L-BFGS for Nonlinear Time Integration 非线性时间积分的具有Cholesky和L-BFGS的碰撞感知拟牛顿求解器

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488297

Bethany Witemeyer, Nicholas J. Weidner, T. Davis, Theodore Kim, S. Sueda

引用次数: 1

Assessing the Impact of Mixed Reality Immersion on Presence and Embodiment 评估混合现实沉浸对在场和化身的影响

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488304

Alexandre Abbey, Thibault Porssut, B. Herbelin, R. Boulic

引用次数: 2

GarMatNet: A Learning-based Method for Predicting 3D Garment Mesh with Parameterized Materials GarMatNet:一种基于学习的参数化材料三维服装网格预测方法

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488294

Z. Luo, Tianxing Li, T. Kanai

{"title":"GarMatNet: A Learning-based Method for Predicting 3D Garment Mesh with Parameterized Materials","authors":"Z. Luo, Tianxing Li, T. Kanai","doi":"10.1145/3487983.3488294","DOIUrl":"https://doi.org/10.1145/3487983.3488294","url":null,"abstract":"Recent progress in learning-based methods of garment mesh generation is resulting in increased efficiency and maintenance of reality during the generation process. However, none of the previous works so far have focused on variations in material types based on a parameterized material parameter under static poses. In this work, we propose a learning-based method, GarMatNet, for predicting garment deformation based on the functions of human poses and garment materials while maintaining detailed garment wrinkles. GarMatNet consists of two components: a generally-fitting network for predicting smoothed garment mesh and a locally-detailed network for adding detailed wrinkles based on smoothed garment mesh. We hypothesize that material properties play an essential role in the deformation of garments. Since the influences of material type are relatively smaller than pose or body shape, we employ linear interpolation among different factors to control deformation. More specifically, we apply a parameterized material space based on the mass-spring model to express the difference between materials and construct a suitable network structure with weight adjustment between material properties and poses. The experimental results demonstrate that GarMatNet is comparable to the physically-based simulation (PBS) prediction and offers advantages regarding generalization ability, model size, and training time over the baseline model.","PeriodicalId":170509,"journal":{"name":"Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Does Synthetic Voice alter Social Response to a Photorealistic Character in Virtual Reality? 合成声音是否会改变虚拟现实中对逼真人物的社会反应?

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488296

Katja Zibrek, João P. Cabral, R. Mcdonnell

引用次数: 6

Interactive Viewpoint Exploration for Constructing View-Dependent Models 构建视依赖模型的交互式视点探索

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488287

Tsukasa Fukusato, Akinobu Maejima

引用次数: 1

Interactive Simulation of Disease Contagion in Dynamic Crowds 动态人群中疾病传染的交互模拟

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488298

Alon Flor, Chengguizi Han, Tao Xue, Mridul Aanjaneya

引用次数: 0

SNAP:Successor Entropy based Incremental Subgoal Discovery for Adaptive Navigation SNAP:基于后继熵的自适应导航增量子目标发现

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488292

R. Dubey, Samuel S. Sohn, J. Abualdenien, Tyler Thrash, C. Hoelscher, A. Borrmann, Mubbasir Kapadia

{"title":"SNAP:Successor Entropy based Incremental Subgoal Discovery for Adaptive Navigation","authors":"R. Dubey, Samuel S. Sohn, J. Abualdenien, Tyler Thrash, C. Hoelscher, A. Borrmann, Mubbasir Kapadia","doi":"10.1145/3487983.3488292","DOIUrl":"https://doi.org/10.1145/3487983.3488292","url":null,"abstract":"Reinforcement learning (RL) has demonstrated great success in solving navigation tasks but often fails when learning complex environmental structures. One open challenge is to incorporate low-level generalizable skills with human-like adaptive path-planning in an RL framework. Motivated by neural findings in animal navigation, we propose a Successor eNtropy-based Adaptive Path-planning (SNAP) that combines a low-level goal-conditioned policy with the flexibility of a classical high-level planner. SNAP decomposes distant goal-reaching tasks into multiple nearby goal-reaching sub-tasks using a topological graph. To construct this graph, we propose an incremental subgoal discovery method that leverages the highest-entropy states in the learned Successor Representation. The Successor Representation encodes the likelihood of being in a future state given the current state and capture the relational structure of states based on a policy. Our main contributions lie in discovering subgoal states that efficiently abstract the state-space and proposing a low-level goal-conditioned controller for local navigation. Since the basic low-level skill is learned independent of state representation, our model easily generalizes to novel environments without intensive relearning. We provide empirical evidence that the proposed method enables agents to perform long-horizon sparse reward tasks quickly, take detours during barrier tasks, and exploit shortcuts that did not exist during training. Our experiments further show that the proposed method outperforms the existing goal-conditioned RL algorithms in successfully reaching distant-goal tasks and policy learning. To evaluate human-like adaptive path-planning, we also compare our optimal agent with human data and found that, on average, the agent was able to find a shorter path than the human participants.","PeriodicalId":170509,"journal":{"name":"Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129521727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

PSM: Parametric Saliency Maps for Autonomous Pedestrians PSM:自动行人的参数显著性映射

Proceedings of the 14th ACM SIGGRAPH Conference on Motion, Interaction and Games Pub Date : 2021-11-10 DOI: 10.1145/3487983.3488299

Melissa Kremer, Peter Caruana, M. B. Haworth, Mubbasir Kapadia, P. Faloutsos

引用次数: 4