Publications

@article{ghasem_player_2021,

title = {Player Tracking Technology and Data for Injury Prevention in the National Football League},

author = {Wesley Ghasem and Joshua Valenzuela and Leslie A. Saxon},

url = {https://journals.lww.com/10.1249/JSR.0000000000000873},

doi = {10.1249/JSR.0000000000000873},

issn = {1537-8918},

year  = {2021},

date = {2021-09-01},

urldate = {2022-09-28},

journal = {Curr Sports Med Rep},

volume = {20},

number = {9},

pages = {436--439},

keywords = {CBC},

pubstate = {published},

tppubtype = {article}

}

@article{schuster_kinematics_2021,

title = {Kinematics and observer-animator kinematic similarity predict mental state attribution from Heider–Simmel style animations},

author = {Bianca A. Schuster and Dagmar S. Fraser and Jasper J. F. Bosch and Sophie Sowden and Andrew S. Gordon and Dongsung Huh and Jennifer L. Cook},

url = {https://www.nature.com/articles/s41598-021-97660-2},

doi = {10.1038/s41598-021-97660-2},

issn = {2045-2322},

year  = {2021},

date = {2021-09-01},

urldate = {2022-09-29},

journal = {Sci Rep},

volume = {11},

number = {1},

pages = {18266},

abstract = {The ability to ascribe mental states, such as beliefs or desires to oneself and other individuals forms an integral part of everyday social interaction. Animations tasks, in which observers watch videos of interacting triangles, have been extensively used to test mental state attribution in a variety of clinical populations. Compared to control participants, individuals with clinical conditions such as autism typically offer less appropriate mental state descriptions of such videos. Recent research suggests that stimulus kinematics and movement similarity (between the video and the observer) may contribute to mental state attribution difficulties. Here we present a novel adaptation of the animations task, suitable to track and compare animation generator and -observer kinematics. Using this task and a population-derived stimulus database, we confirmed the hypotheses that an animation’s jerk and jerk similarity between observer and animator significantly contribute to the correct identification of an animation. By employing random forest analysis to explore other stimulus characteristics, we reveal that other indices of movement similarity, including acceleration- and rotation-based similarity, also predict performance. Our results highlight the importance of movement similarity between observer and animator and raise new questions about reasons why some clinical populations exhibit difficulties with this task.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{rizzo_recent_2021,

title = {Recent advances in virtual reality and psychology: Introduction to the special issue.},

author = {Albert “Skip” Rizzo and Grace J. Goodwin and Alyssa N. De Vito and Joshua D. Bell},

url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/tps0000316},

doi = {10.1037/tps0000316},

issn = {2332-2179, 2332-2136},

year  = {2021},

date = {2021-09-01},

urldate = {2022-09-13},

journal = {Translational Issues in Psychological Science},

volume = {7},

number = {3},

pages = {213--217},

keywords = {DTIC, MedVR, VR},

pubstate = {published},

tppubtype = {article}

}

@article{awada_working_2021,

title = {Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience},

author = {Mohamad Awada and Gale Lucas and Burcin Becerik-Gerber and Shawn Roll},

url = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/WOR-210301},

doi = {10.3233/WOR-210301},

issn = {10519815, 18759270},

year  = {2021},

date = {2021-08-01},

urldate = {2022-09-26},

journal = {WOR},

volume = {69},

number = {4},

pages = {1171--1189},

abstract = {BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.},

keywords = {DTIC, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{dukes_rise_2021,

title = {The rise of affectivism},

author = {Daniel Dukes and Kathryn Abrams and Ralph Adolphs and Mohammed E. Ahmed and Andrew Beatty and Kent C. Berridge and Susan Broomhall and Tobias Brosch and Joseph J. Campos and Zanna Clay and Fabrice Clément and William A. Cunningham and Antonio Damasio and Hanna Damasio and Justin D’Arms and Jane W. Davidson and Beatrice Gelder and Julien Deonna and Ronnie Sousa and Paul Ekman and Phoebe C. Ellsworth and Ernst Fehr and Agneta Fischer and Ad Foolen and Ute Frevert and Didier Grandjean and Jonathan Gratch and Leslie Greenberg and Patricia Greenspan and James J. Gross and Eran Halperin and Arvid Kappas and Dacher Keltner and Brian Knutson and David Konstan and Mariska E. Kret and Joseph E. LeDoux and Jennifer S. Lerner and Robert W. Levenson and George Loewenstein and Antony S. R. Manstead and Terry A. Maroney and Agnes Moors and Paula Niedenthal and Brian Parkinson and Ioannis Pavlidis and Catherine Pelachaud and Seth D. Pollak and Gilles Pourtois and Birgitt Roettger-Roessler and James A. Russell and Disa Sauter and Andrea Scarantino and Klaus R. Scherer and Peter Stearns and Jan E. Stets and Christine Tappolet and Fabrice Teroni and Jeanne Tsai and Jonathan Turner and Carien Van Reekum and Patrik Vuilleumier and Tim Wharton and David Sander},

url = {http://www.nature.com/articles/s41562-021-01130-8},

doi = {10.1038/s41562-021-01130-8},

issn = {2397-3374},

year  = {2021},

date = {2021-07-01},

urldate = {2022-09-28},

journal = {Nat Hum Behav},

volume = {5},

number = {7},

pages = {816--820},

keywords = {Emotions},

pubstate = {published},

tppubtype = {article}

}

@article{stocco_analysis_2021-1,

title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},

author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},

doi = {10.1016/j.neuroimage.2021.118035},

issn = {10538119},

year  = {2021},

date = {2021-07-01},

urldate = {2021-04-30},

journal = {NeuroImage},

volume = {235},

pages = {118035},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{stocco_analysis_2021,

title = {Analysis of the human connectome data supports the notion of a “Common Model of Cognition” for human and human-like intelligence across domains},

author = {Andrea Stocco and Catherine Sibert and Zoe Steine-Hanson and Natalie Koh and John E. Laird and Christian J. Lebiere and Paul Rosenbloom},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1053811921003128},

doi = {10.1016/j.neuroimage.2021.118035},

issn = {10538119},

year  = {2021},

date = {2021-07-01},

urldate = {2021-05-06},

journal = {NeuroImage},

volume = {235},

pages = {118035},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{horstmann_i_2021,

title = {I Just Wanna Blame Somebody, Not Something! Reactions to a Computer Agent Giving Negative Feedback Based on the Instructions of a Person},

author = {Aike C. Horstmann and Jonathan Gratch and Nicole C. Krämer},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1071581921001014},

doi = {10.1016/j.ijhcs.2021.102683},

issn = {10715819},

year  = {2021},

date = {2021-06-01},

urldate = {2021-06-18},

journal = {International Journal of Human-Computer Studies},

pages = {102683},

abstract = {Previous research focused on differences between interacting with a person-controlled avatar and a computer-controlled virtual agent. This study however examines an aspiring form of technology called agent representative which constitutes a mix of the former two interaction partner types since it is a computer agent which was previously instructed by a person to take over a task on the person’s behalf. In an experimental lab study with a 2 x 3 between-subjects-design (N = 195), people believed to study together either with an agent representative, avatar, or virtual agent. The interaction partner was described to either possess high or low expertise, while always giving negative feedback regarding the participant’s performance. Results show small but interesting differences regarding the type of agency. People attributed the most agency and blame to the person(s) behind the software and reported the most negative affect when interacting with an avatar, which was less the case for a person’s agent representative and the least for a virtual agent. Level of expertise had no significant effect and other evaluation measures were not affected.},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{aryal_intelligent_2021,

title = {Intelligent Agents to Improve Thermal Satisfaction by Controlling Personal Comfort Systems Under Different Levels of Automation},

author = {Ashrant Aryal and Burcin Becerik-Gerber and Gale M. Lucas and Shawn C. Roll},

url = {https://ieeexplore.ieee.org/document/9260148/},

doi = {10.1109/JIOT.2020.3038378},

issn = {2327-4662, 2372-2541},

year  = {2021},

date = {2021-04-01},

urldate = {2022-10-24},

journal = {IEEE Internet Things J.},

volume = {8},

number = {8},

pages = {7089--7100},

keywords = {AI},

pubstate = {published},

tppubtype = {article}

}

@article{de_melo_heuristic_2021-1,

title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},

author = {Celso M. Melo and Jonathan Gratch and Frank Krueger},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004221001966},

doi = {10.1016/j.isci.2021.102228},

issn = {25890042},

year  = {2021},

date = {2021-03-01},

urldate = {2021-04-14},

journal = {iScience},

volume = {24},

number = {3},

pages = {102228},

abstract = {Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mell_expert-model_2021,

title = {An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied data},

author = {Johnathan Mell and Markus Beissinger and Jonathan Gratch},

url = {http://link.springer.com/10.1007/s12193-021-00368-w},

doi = {10.1007/s12193-021-00368-w},

issn = {1783-7677, 1783-8738},

year  = {2021},

date = {2021-03-01},

urldate = {2021-04-15},

journal = {J Multimodal User Interfaces},

abstract = {We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other techniques (such as linear regression models or boosted decision trees). In a follow-up study, we show that the most successful models change as the dataset size increases and the prediction targets change, and show that boosted decision trees may not be suitable for the negotiation domain. We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation. Further, we show that this approach can be a stepping stone from purely exploratory research to targeted human-behavioral experimentation. Through our approach, areas of social artificial intelligence that have historically benefited from expert knowledge and traditional AI approaches can be combined with more recent proven-effective machine learning algorithms.},

keywords = {DTIC, Machine Learning, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{gramlich_psychophysiology_2021,

title = {Psychophysiology during exposure to trauma memories: Comparative effects of virtual reality and imaginal exposure for posttraumatic stress disorder},

author = {Michael A. Gramlich and Derek J. Smolenski and Aaron M. Norr and Barbara O. Rothbaum and Albert A. Rizzo and Frank Andrasik and Emily Fantelli and Greg M. Reger},

url = {https://onlinelibrary.wiley.com/doi/10.1002/da.23141},

doi = {10.1002/da.23141},

issn = {1091-4269, 1520-6394},

year  = {2021},

date = {2021-03-01},

urldate = {2021-04-14},

journal = {Depression and Anxiety},

pages = {da.23141},

abstract = {Background: This investigation involved an in‐depth examination of psychophysiological responses during exposure to the trauma memory across 10 sessions among active duty soldiers with combat‐related posttraumatic stress disorder (PTSD) treated by Prolonged Exposure (PE) or Virtual Reality Exposure (VRE). We compared psychophysiological changes, session‐by‐session, between VRE and traditional imaginal exposure. Methods: Heart rate (HR), galvanic skin response (GSR), and peripheral skin temperature were collected every 5 min during exposure sessions with 61 combat veterans of Iraq/Afghanistan and compared to the PTSD Checklist (PCL‐C) and Clinician‐Administered PTSD Scale (CAPS) outcomes using multilevel modeling. Results: Over the course of treatment, participants in the PE group had higher HR arousal compared to participants in the VRE group. With reference to GSR, in earlier sessions, participants demonstrated a within‐session increase, whereas, in later sessions, participants showed a within‐session habituation response. A significant interaction was found for GSR and treatment assignment for within‐session change, withinperson effect, predicting CAPS (d = 0.70) and PCL‐C (d = 0.66) outcomes. Conclusion: Overall, these findings suggest that exposure to traumatic memories activates arousal across sessions, with GSR being most associated with reductions in PTSD symptoms for participants in the PE group.},

keywords = {MedVR},

pubstate = {published},

tppubtype = {article}

}

@article{de_melo_heuristic_2021,

title = {Heuristic thinking and altruism toward machines in people impacted by COVID-19},

author = {Celso M. Melo and Jonathan Gratch and Frank Krueger},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004221001966},

doi = {10.1016/j.isci.2021.102228},

issn = {25890042},

year  = {2021},

date = {2021-03-01},

urldate = {2021-04-14},

journal = {iScience},

volume = {24},

number = {3},

pages = {102228},

abstract = {Autonomous machines are poised to become pervasive, but most treat machines differently: we are willing to violate social norms and less likely to display altruism toward machines. Here, we report an unexpected effect that those impacted by COVID-19—as measured by a post-traumatic stress disorder scale—show a sharp reduction in this difference. Participants engaged in the dictator game with humans and machines and, consistent with prior research on disasters, those impacted by COVID-19 displayed more altruism to other humans. Unexpectedly, participants impacted by COVID-19 displayed equal altruism toward human and machine partners. A mediation analysis suggests that altruism toward machines was explained by an increase in heuristic thinking—reinforcing prior theory that heuristic thinking encourages people to treat machines like people—and faith in technology—perhaps reflecting long-term consequences on how we act with machines. These findings give insight, but also raise concerns, for the design of technology.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{awada_integrated_2021,

title = {An integrated emotional and physiological assessment for VR-based active shooter incident experiments},

author = {Mohamad Awada and Runhe Zhu and Burcin Becerik-Gerber and Gale Lucas and Erroll Southers},

url = {https://linkinghub.elsevier.com/retrieve/pii/S1474034620301968},

doi = {10.1016/j.aei.2020.101227},

issn = {14740346},

year  = {2021},

date = {2021-01-01},

urldate = {2022-10-24},

journal = {Advanced Engineering Informatics},

volume = {47},

pages = {101227},

keywords = {DTIC, VR},

pubstate = {published},

tppubtype = {article}

}

@article{chen_ground_2021,

title = {Ground material classification and for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach},

author = {Meida Chen and Andrew Feng and Yu Hou and Kyle McCullough and Pratusha Bhuvana Prasad and Lucio Soibelman},

url = {https://arxiv.org/abs/2109.12221},

doi = {10.48550/ARXIV.2109.12221},

year  = {2021},

date = {2021-01-01},

urldate = {2022-09-27},

abstract = {In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided additional high-resolution imaging capabilities with low cost for mapping a relatively large area of interest. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations. Our previous works have demonstrated the importance of information extraction from the derived photogrammetric data to create semantic-rich virtual environments (Chen et al., 2019). For example, an increase of simulation realism and fidelity was achieved by segmenting and replacing photogrammetric trees with game-ready tree models. In this work, we further investigated the semantic information extraction problem and focused on the ground material segmentation and object detection tasks. The main innovation of this work was that we leveraged both the original 2D images and the derived 3D photogrammetric data to overcome the challenges faced when using each individual data source. For ground material segmentation, we utilized an existing convolutional neural network architecture (i.e., 3DMV) which was originally designed for segmenting RGB-D sensed indoor data. We improved its performance for outdoor photogrammetric data by introducing a depth pooling layer in the architecture to take into consideration the distance between the source images and the reconstructed terrain model. To test the performance of our improved 3DMV, a ground truth ground material database was created using data from the One World Terrain (OWT) data repository. Finally, a workflow for importing the segmented ground materials into a virtual simulation scene was introduced, and visual results are reported in this paper.},

keywords = {DTIC, Simulation, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{lee_comparing_2021,

title = {Comparing mind perception in strategic exchanges: human-agent negotiation, dictator and ultimatum games},

author = {Minha Lee and Gale Lucas and Jonathan Gratch},

url = {http://link.springer.com/10.1007/s12193-020-00356-6},

doi = {10.1007/s12193-020-00356-6},

issn = {1783-7677, 1783-8738},

year  = {2021},

date = {2021-01-01},

urldate = {2021-04-15},

journal = {J Multimodal User Interfaces},

abstract = {Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In a comparative manner, we observed how perceived minds of agents shape people’s behavior in the dictator game, ultimatum game, and negotiation against artificial agents. To do so, we varied agents’ minds on two dimensions of the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude) via descriptions and dialogs. In our first study, agents with emotional capacity garnered more allocations in the dictator game, but in the ultimatum game, agents’ described agency and affective capacity, both led to greater offers. In the second study on negotiation, agents ascribed with low-agency traits earned more points than those with high-agency traits, though the negotiation tactic was the same for all agents. Although patiency did not impact game points, participants sent more happy and surprise emojis and emotionally valenced messages to agents that demonstrated emotional capacity during negotiations. Further, our exploratory analyses indicate that people related only to agents with perceived affective aptitude across all games. Both perceived agency and affective capacity contributed to moral standing after dictator and ultimatum games. But after negotiations, only agents with perceived affective capacity were granted moral standing. Manipulating mind dimensions of machines has differing effects on how people react to them in dictator and ultimatum games, compared to a more complex economic exchange like negotiation. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations, in contrast with simple economic games.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{gratch_promise_2021,

title = {The Promise and Peril of Automated Negotiators},

author = {Jonathan Gratch},

url = {https://onlinelibrary.wiley.com/doi/10.1111/nejo.12348},

doi = {10.1111/nejo.12348},

issn = {0748-4526, 1571-9979},

year  = {2021},

date = {2021-01-01},

urldate = {2021-04-14},

journal = {Negotiation Journal},

volume = {37},

number = {1},

pages = {13--34},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{de_melo_risk_2021,

title = {Risk of Injury in Moral Dilemmas With Autonomous Vehicles},

author = {Celso M. Melo and Stacy Marsella and Jonathan Gratch},

url = {https://www.frontiersin.org/articles/10.3389/frobt.2020.572529/full},

doi = {10.3389/frobt.2020.572529},

issn = {2296-9144},

year  = {2021},

date = {2021-01-01},

urldate = {2021-04-14},

journal = {Front. Robot. AI},

volume = {7},

pages = {572529},

abstract = {As autonomous machines, such as automated vehicles (AVs) and robots, become pervasive in society, they will inevitably face moral dilemmas where they must make decisions that risk injuring humans. However, prior research has framed these dilemmas in starkly simple terms, i.e., framing decisions as life and death and neglecting the influence of risk of injury to the involved parties on the outcome. Here, we focus on this gap and present experimental work that systematically studies the effect of risk of injury on the decisions people make in these dilemmas. In four experiments, participants were asked to program their AVs to either save five pedestrians, which we refer to as the utilitarian choice, or save the driver, which we refer to as the nonutilitarian choice. The results indicate that most participants made the utilitarian choice but that this choice was moderated in important ways by perceived risk to the driver and risk to the pedestrians. As a second contribution, we demonstrate the value of formulating AV moral dilemmas in a game-theoretic framework that considers the possible influence of others’ behavior. In the fourth experiment, we show that participants were more (less) likely to make the utilitarian choice, the more utilitarian (nonutilitarian) other drivers behaved; furthermore, unlike the game-theoretic prediction that decision-makers inevitably converge to nonutilitarianism, we found significant evidence of utilitarianism. We discuss theoretical implications for our understanding of human decision-making in moral dilemmas and practical guidelines for the design of autonomous machines that solve these dilemmas while, at the same time, being likely to be adopted in practice.},

keywords = {Autonomous Vehicles, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{mozgai_user-centered_2021,

title = {User-Centered Design Model for Mobile Health (mHealth) Applications: A Military Case Study in Rapid Assessment Process (RAP)},

author = {Sharon Mozgai and Brian Femminella and Arno Hartholt and Skip Rizzo},

url = {https://uploads-ssl.webflow.com/5f11f7e80d5a3b6dfdeeb614/5f9b3284d3d73e1da6a8f848_CHI_2021_Battle%20Buddy.pdf},

year  = {2021},

date = {2021-01-01},

pages = {10},

abstract = {CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing design and evaluation methods; HCI design and evaluation methods; User centered design; • Applied computing → Military; • Computing methodologies → Intelligent agents.},

keywords = {ARL, MedVR},

pubstate = {published},

tppubtype = {article}

}

@article{gratch_field_2021,

title = {The field of Affective Computing: An interdisciplinary perspective},

author = {Jonathan Gratch},

url = {https://people.ict.usc.edu/~gratch/CSCI534/Readings/Gratch%20-%20The%20field%20of%20affective%20computing.pdf},

year  = {2021},

date = {2021-01-01},

journal = {Transactions of the Japanese Society for Artificial Intelligence},

volume = {36},

number = {1},

pages = {13},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {article}

}

@article{chen_3d_2020,

title = {3D Photogrammetry Point Cloud Segmentation Using a Model Ensembling Framework},

author = {Meida Chen and Andrew Feng and Kyle McCullough and Pratusha Bhuvana Prasad and Ryan McAlinden and Lucio Soibelman},

url = {http://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0000929},

doi = {10.1061/(ASCE)CP.1943-5487.0000929},

issn = {0887-3801, 1943-5487},

year  = {2020},

date = {2020-11-01},

journal = {Journal of Computing in Civil Engineering},

volume = {34},

number = {6},

abstract = {The US Army is paying increased attention to the development of rapid three-dimensional (3D) reconstruction using photogrammetry and unmanned aerial vehicle (UAV) technologies for creating virtual environments and simulations in areas of interest. The ability of the intelligence community, mission commanders, and front-line soldiers to understand their deployed physical environment in advance is critical in the planning and rehearsal phases of any military operation. In order to achieve various simulation capabilities such as destruction operations, route planning, and explosive-standoff distances computation among others, reconstructed 3D data needs to be properly attributed. In this paper, we introduce a model ensembling framework for segmenting a 3D photogrammetry point cloud into top-level terrain elements (i.e., ground, human-made objects, and vegetation). Preprocessing and postprocessing methods were designed to overcome the data segmentation challenges posed by photogrammetric data-quality issues. A large UAV-based photogrammetric database was created for validation purposes. The designed model ensembling framework was compared with existing point cloud segmentation algorithms, and it outperformed other algorithms and achieved the best F1-score. Because the ultimate goal of segmenting a photogrammetric-generated point cloud is to create realistic virtual environments for simulation. Qualitative results for creating virtual environments using the segmented data are also discussed in this paper. DOI: 10.1061/(ASCE)CP.1943-5487.0000929. © 2020 American Society of Civil Engineers.},

keywords = {Narrative, STG, UARC},

pubstate = {published},

tppubtype = {article}

}

@article{li_dynamic_2020,

title = {Dynamic Facial Asset and Rig Generation from a Single Scan},

author = {Jiaman Li and Zhengfei Kuang and Yajie Zhao and Mingming He and Karl Bladin and Hao Li},

url = {https://dl.acm.org/doi/10.1145/3414685.3417817},

doi = {doi/10.1145/3414685.3417817},

year  = {2020},

date = {2020-11-01},

journal = {ACM Transactions on Graphics},

volume = {39},

number = {6},

abstract = {The creation of high-fidelity computer-generated (CG) characters for films and games is tied with intensive manual labor, which involves the creation of comprehensive facial assets that are often captured using complex hardware. To simplify and accelerate this digitization process, we propose a framework for the automatic generation of high-quality dynamic facial models, including rigs which can be readily deployed for artists to polish. Our framework takes a single scan as input to generate a set of personalized blendshapes, dynamic textures, as well as secondary facial components (e.g., teeth and eyeballs). Based on a facial database with over 4, 000 scans with pore-level details, varying expressions and identities, we adopt a self-supervised neural network to learn personalized blendshapes from a set of template expressions. We also model the joint distribution between identities and expressions, enabling the inference of a full set of personalized blendshapes with dynamic appearances from a single neutral input scan. Our generated personalized face rig assets are seamlessly compatible with professional production pipelines for facial animation and rendering. We demonstrate a highly robust and effective framework on a wide range of subjects, and showcase high-fidelity facial animations with automatically generated personalized dynamic textures.},

keywords = {ARO-Coop, Graphics},

pubstate = {published},

tppubtype = {article}

}