Publications

@misc{han_-depth_2024,

title = {In-Depth Analysis of Emotion Recognition through Knowledge-Based Large Language Models},

author = {Bin Han and Cleo Yau and Su Lei and Jonathan Gratch},

url = {http://arxiv.org/abs/2408.00780},

year  = {2024},

date = {2024-07-01},

urldate = {2024-08-15},

publisher = {arXiv},

abstract = {Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.},

note = {arXiv:2408.00780 [cs]},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{yin_validation_2024,

title = {Validation and Evacuee Modeling of Virtual Robot-guided Emergency Evacuation Experiments},

author = {Yinxuan Yin and Mollik Nayyar and Daniel Holman and Gale Lucas and Colin Holbrook and Alan Wagner},

url = {https://osf.io/mr78s},

doi = {10.31234/osf.io/mr78s},

year  = {2024},

date = {2024-06-01},

urldate = {2024-09-17},

publisher = {Center for Open Science},

abstract = {Virtual Reality (VR) is an increasingly common tool for investigating human responses to emergency situations. Nonetheless, studies validating and comparing human subject behavior during real world emergencies to their responses in VR are notably rare, and no prior studies have validated whether human emergency responses to guidance from a robot are comparable in VR versus the real world. In the present pre-registered study, we used VR to replicate a previous robot- guided emergency evacuation study conducted in the real world and compared human subject behavior in matched physical and virtual environments. In both environments, human subjects were asked to follow a robot to a location and to then read an article. While reading, a fire alarm sounds. The robot then attempted to guide them to a distant, unfamiliar exit rather than nearby and familiar exits. We observed close correspondences between evacuee exit choice (the robot’s distant exit versus closer exits), evacuation time, and trust in the robot between the VR and physical environments. We further demonstrate that data collected in virtual reality can be used to create accurate motion models (mean error of 0.42 centimeters) predicting evacuee trajectories and locations in real life. Taken together, the results provide evidence for the ecological validity of VR approaches to studying human-robot interaction, particularly robot- guided emergency evacuation.},

keywords = {DTIC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{zhang_magicpose4d_2024,

title = {MagicPose4D: Crafting Articulated Models with Appearance and Motion Control},

author = {Hao Zhang and Di Chang and Fang Li and Mohammad Soleymani and Narendra Ahuja},

url = {https://arxiv.org/abs/2405.14017},

doi = {10.48550/ARXIV.2405.14017},

year  = {2024},

date = {2024-05-01},

urldate = {2024-06-25},

publisher = {arXiv},

abstract = {With the success of 2D and 3D visual generative models, there is growing interest in generating 4D content. Existing methods primarily rely on text prompts to produce 4D content, but they often fall short of accurately defining complex or rare motions. To address this limitation, we propose MagicPose4D, a novel framework for refined control over both appearance and motion in 4D generation. Unlike traditional methods, MagicPose4D accepts monocular videos as motion prompts, enabling precise and customizable motion generation. MagicPose4D comprises two key modules: 

 i) Dual-Phase 4D Reconstruction Modulevphantom which operates in two phases. The first phase focuses on capturing the model's shape using accurate 2D supervision and less accurate but geometrically informative 3D pseudo-supervision without imposing skeleton constraints. The second phase refines the model using more accurate pseudo-3D supervision, obtained in the first phase and introduces kinematic chain-based skeleton constraints to ensure physical plausibility. Additionally, we propose a Global-local Chamfer loss that aligns the overall distribution of predicted mesh vertices with the supervision while maintaining part-level alignment without extra annotations. 

 ii) Cross-category Motion Transfer Modulevphantom leverages the predictions from the 4D reconstruction module and uses a kinematic-chain-based skeleton to achieve cross-category motion transfer. It ensures smooth transitions between frames through dynamic rigidity, facilitating robust generalization without additional training. 

 Through extensive experiments, we demonstrate that MagicPose4D significantly improves the accuracy and consistency of 4D content generation, outperforming existing methods in various benchmarks.},

note = {Version Number: 1},

keywords = {VGL, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{west_improving_2024,

title = {Improving Social Connection with Weak Ties and Strangers: Effects of a New Micro-Intervention on Interaction Quality and Social Behavior},

author = {Taylor Nicole West and Michael Prinzing and Catherine Garton and Catherine J. Berman and Jieni Zhou and James Hale and Jonathan Gratch and Barbara Fredrickson},

url = {https://osf.io/ytjr6},

doi = {10.31234/osf.io/ytjr6},

year  = {2024},

date = {2024-05-01},

urldate = {2024-06-25},

abstract = {We propose that the emotional quality of people’s interactions with acquaintances (i.e., weak ties) and strangers contributes to well-being. We test whether a new micro-intervention can raise the quality of these interactions. We randomized young adults (N = 335) to this connectedness micro-intervention or a control intervention. Both interventions were delivered via a psychoeducational video followed by a brief conversation with a virtual human, with whom participants developed if-then plans to carry out their assigned behavioral goal. Pre-intervention, high-quality weak-tie and stranger interactions were associated with lower loneliness and greater mental health independent of strong-tie interaction quality. Experimental data showed the connectedness intervention improved the emotional quality of participants' interactions with weak ties and strangers over two days, evident in participants’ episodic self-reports and faster in-lab conversational response time. Discussion centers on implications for developing scalable behavioral interventions to improve well-being.},

keywords = {Emotions, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{tran_dyadic_2024,

title = {Dyadic Interaction Modeling for Social Behavior Generation},

author = {Minh Tran and Di Chang and Maksim Siniukov and Mohammad Soleymani},

url = {http://arxiv.org/abs/2403.09069},

year  = {2024},

date = {2024-03-01},

urldate = {2024-03-19},

publisher = {arXiv},

abstract = {Human-human communication is like a delicate dance where listeners and speakers concurrently interact to maintain conversational dynamics. Hence, an effective model for generating listener nonverbal behaviors requires understanding the dyadic context and interaction. In this paper, we present an effective framework for creating 3D facial motions in dyadic interactions. Existing work consider a listener as a reactive agent with reflexive behaviors to the speaker's voice and facial motions. The heart of our framework is Dyadic Interaction Modeling (DIM), a pre-training approach that jointly models speakers' and listeners' motions through masking and contrastive learning to learn representations that capture the dyadic context. To enable the generation of non-deterministic behaviors, we encode both listener and speaker motions into discrete latent representations, through VQ-VAE. The pre-trained model is further fine-tuned for motion generation. Extensive experiments demonstrate the superiority of our framework in generating listener motions, establishing a new state-of-the-art according to the quantitative measures capturing the diversity and realism of generated motions. Qualitative results demonstrate the superior capabilities of the proposed approach in generating diverse and realistic expressions, eye blinks and head gestures.},

note = {arXiv:2403.09069 [cs]},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{lu_leveraging_2024,

title = {Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection},

author = {Liupei Lu and Yufeng Yin and Yuming Gu and Yizhen Wu and Pratusha Prasad and Yajie Zhao and Mohammad Soleymani},

url = {http://arxiv.org/abs/2403.10737},

year  = {2024},

date = {2024-03-01},

urldate = {2024-04-16},

publisher = {arXiv},

abstract = {Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-source domain adaptation (MSDA) to address the problems of the scarcity of labeled data and the diversity of subjects. Specifically, we propose to generate a diverse dataset through synthetic facial expression re-targeting by transferring the expressions from real faces to synthetic avatars. Then, we use MSDA to transfer the AU detection knowledge from a real dataset and the synthetic dataset to a target dataset. Instead of aligning the overall distributions of different domains, we propose Paired Moment Matching (PM2) to align the features of the paired real and synthetic data with the same facial expression. To further improve gender fairness, PM2 matches the features of the real data with a female and a male synthetic image. Our results indicate that synthetic data and the proposed model improve both AU detection performance and fairness across genders, demonstrating its potential to solve AU detection in-the-wild.},

note = {arXiv:2403.10737 [cs]},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{kwon_are_2024,

title = {Are LLMs Effective Negotiators? Systematic Evaluation of the Multifaceted Capabilities of LLMs in Negotiation Dialogues},

author = {Deuksin Kwon and Emily Weiss and Tara Kulshrestha and Kushal Chawla and Gale M. Lucas and Jonathan Gratch},

url = {http://arxiv.org/abs/2402.13550},

year  = {2024},

date = {2024-02-01},

urldate = {2024-03-14},

publisher = {arXiv},

abstract = {A successful negotiation demands a deep comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, as well as strategic reasoning and effective communication, making it challenging for automated systems. Given the remarkable performance of LLMs across a variety of NLP tasks, in this work, we aim to understand how LLMs can advance different aspects of negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. To this end, we devise a methodology to analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios covering all the time stages of a typical negotiation interaction. Our analysis adds to the increasing evidence for the superiority of GPT-4 across various tasks while also providing insights into specific tasks that remain difficult for LLMs. For instance, the models correlate poorly with human players when making subjective assessments about the negotiation dialogues and often struggle to generate responses that are contextually appropriate as well as strategically advantageous.},

note = {arXiv:2402.13550 [cs]},

keywords = {AI, DTIC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{shi_build_2024,

title = {Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education},

author = {Zhonghao Shi and Allison O'Connell and Zongjian Li and Siqi Liu and Jennifer Ayissi and Guy Hoffman and Mohammad Soleymani and Maja J. Matarić},

url = {http://arxiv.org/abs/2402.01647},

year  = {2024},

date = {2024-01-01},

urldate = {2024-02-21},

publisher = {arXiv},

abstract = {As artificial intelligence (AI) is playing an increasingly important role in our society and global economy, AI education and literacy have become necessary components in college and K-12 education to prepare students for an AI-powered society. However, current AI curricula have not yet been made accessible and engaging enough for students and schools from all socio-economic backgrounds with different educational goals. In this work, we developed an open-source learning module for college and high school students, which allows students to build their own robot companion from the ground up. This open platform can be used to provide hands-on experience and introductory knowledge about various aspects of AI, including robotics, machine learning (ML), software engineering, and mechanical engineering. Because of the social and personal nature of a socially assistive robot companion, this module also puts a special emphasis on human-centered AI, enabling students to develop a better understanding of human-AI interaction and AI ethics through hands-on learning activities. With open-source documentation, assembling manuals and affordable materials, students from different socio-economic backgrounds can personalize their learning experience based on their individual educational goals. To evaluate the student-perceived quality of our module, we conducted a usability testing workshop with 15 college students recruited from a minority-serving institution. Our results indicate that our AI module is effective, easy-to-follow, and engaging, and it increases student interest in studying AI/ML and robotics in the future. We hope that this work will contribute toward accessible and engaging AI education in human-AI interaction for college and high school students.},

note = {arXiv:2402.01647 [cs]},

keywords = {DTIC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{chang_magicdance_2023,

title = {MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer},

author = {Di Chang and Yichun Shi and Quankai Gao and Jessica Fu and Hongyi Xu and Guoxian Song and Qing Yan and Xiao Yang and Mohammad Soleymani},

url = {http://arxiv.org/abs/2311.12052},

year  = {2023},

date = {2023-11-01},

urldate = {2023-12-07},

publisher = {arXiv},

abstract = {In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos. Specifically, we aim to generate human dance videos of any target identity driven by novel pose sequences while keeping the identity unchanged. To this end, we propose a two-stage training strategy to disentangle human motions and appearance (e.g., facial expressions, skin tone and dressing), consisting of the pretraining of an appearance-control block and fine-tuning of an appearance-pose-joint-control block over human dance poses of the same dataset. Our novel design enables robust appearance control with temporally consistent upper body, facial attributes, and even background. The model also generalizes well on unseen human identities and complex motion sequences without the need for any fine-tuning with additional data with diverse human attributes by leveraging the prior knowledge of image diffusion models. Moreover, the proposed model is easy to use and can be considered as a plug-in module/extension to Stable Diffusion. We also demonstrate the model's ability for zero-shot 2D animation generation, enabling not only the appearance transfer from one identity to another but also allowing for cartoon-like stylization given only pose inputs. Extensive experiments demonstrate our superior performance on the TikTok dataset.},

note = {arXiv:2311.12052 [cs]},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{yang_context_2023,

title = {Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models},

author = {Daniel Yang and Aditya Kommineni and Mohammad Alshehri and Nilamadhab Mohanty and Vedant Modi and Jonathan Gratch and Shrikanth Narayanan},

url = {http://arxiv.org/abs/2311.03551},

year  = {2023},

date = {2023-11-01},

urldate = {2023-12-07},

publisher = {arXiv},

abstract = {The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the performance of machine learning models trained on top of them. As re-annotating entire datasets is a costly and time-consuming task that cannot be done at scale, we propose to use the expressive capabilities of large language models to synthesize additional context for input text to increase its alignment with the annotated emotional labels. In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. We provide both human and empirical evaluation to demonstrate the efficacy of the enhanced context. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.},

note = {arXiv:2311.03551 [cs]},

keywords = {AI, DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{cho_can_2023,

title = {Can Language Model Moderators Improve the Health of Online Discourse?},

author = {Hyundong Cho and Shuai Liu and Taiwei Shi and Darpan Jain and Basem Rizk and Yuyang Huang and Zixun Lu and Nuan Wen and Jonathan Gratch and Emilio Ferrara and Jonathan May},

url = {http://arxiv.org/abs/2311.10781},

year  = {2023},

date = {2023-11-01},

urldate = {2023-12-07},

publisher = {arXiv},

abstract = {Human moderation of online conversation is essential to maintaining civility and focus in a dialogue, but is challenging to scale and harmful to moderators. The inclusion of sophisticated natural language generation modules as a force multiplier aid moderators is a tantalizing prospect, but adequate evaluation approaches have so far been elusive. In this paper, we establish a systematic definition of conversational moderation effectiveness through a multidisciplinary lens that incorporates insights from social science. We then propose a comprehensive evaluation framework that uses this definition to asses models' moderation capabilities independently of human intervention. With our framework, we conduct the first known study of conversational dialogue models as moderators, finding that appropriately prompted models can provide specific and fair feedback on toxic behavior but struggle to influence users to increase their levels of respect and cooperation.},

note = {arXiv:2311.10781 [cs]},

keywords = {AI, Dialogue, DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{tran_privacy-preserving_2023,

title = {Privacy-preserving Representation Learning for Speech Understanding},

author = {Minh Tran and Mohammad Soleymani},

url = {http://arxiv.org/abs/2310.17194},

year  = {2023},

date = {2023-10-01},

urldate = {2023-12-07},

publisher = {arXiv},

abstract = {Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained encoder, we train a Transformer to estimate the representations for the same utterances spoken by other speakers. During inference, the extracted representations can be converted into different identities to preserve privacy. We compare the results with the voice anonymization baselines from the VoicePrivacy 2022 challenge. We evaluate our framework on speaker identification for privacy and emotion recognition, depression classification, and intent classification for utility. Our method outperforms the baselines on privacy and utility in paralinguistic tasks and achieves comparable performance for intent classification.},

note = {arXiv:2310.17194 [eess]},

keywords = {DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{chawla_be_2023,

title = {Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions},

author = {Kushal Chawla and Ian Wu and Yu Rong and Gale M. Lucas and Jonathan Gratch},

url = {http://arxiv.org/abs/2310.14404},

year  = {2023},

date = {2023-10-01},

urldate = {2023-12-07},

publisher = {arXiv},

abstract = {A natural way to design a negotiation dialogue system is via self-play RL: train an agent that learns to maximize its performance by interacting with a simulated user that has been designed to imitate human-human dialogue data. Although this procedure has been adopted in prior work, we find that it results in a fundamentally flawed system that fails to learn the value of compromise in a negotiation, which can often lead to no agreements (i.e., the partner walking away without a deal), ultimately hurting the model's overall performance. We investigate this observation in the context of the DealOrNoDeal task, a multi-issue negotiation over books, hats, and balls. Grounded in negotiation theory from Economics, we modify the training procedure in two novel ways to design agents with diverse personalities and analyze their performance with human partners. We find that although both techniques show promise, a selfish agent, which maximizes its own performance while also avoiding walkaways, performs superior to other variants by implicitly learning to generate value for both itself and the negotiation partner. We discuss the implications of our findings for what it means to be a successful negotiation dialogue system and how these systems should be designed in the future.},

note = {arXiv:2310.14404 [cs]},

keywords = {Dialogue, DTIC, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{chang_libreface_2023,

title = {LibreFace: An Open-Source Toolkit for Deep Facial Expression Analysis},

author = {Di Chang and Yufeng Yin and Zongjian Li and Minh Tran and Mohammad Soleymani},

url = {http://arxiv.org/abs/2308.10713},

year  = {2023},

date = {2023-08-01},

urldate = {2024-02-21},

publisher = {arXiv},

abstract = {Facial expression analysis is an important tool for human-computer interaction. In this paper, we introduce LibreFace, an open-source toolkit for facial expression analysis. This open-source toolbox offers real-time and offline analysis of facial behavior through deep learning models, including facial action unit (AU) detection, AU intensity estimation, and facial expression recognition. To accomplish this, we employ several techniques, including the utilization of a large-scale pre-trained network, feature-wise knowledge distillation, and task-specific fine-tuning. These approaches are designed to effectively and accurately analyze facial expressions by leveraging visual information, thereby facilitating the implementation of real-time interactive applications. In terms of Action Unit (AU) intensity estimation, we achieve a Pearson Correlation Coefficient (PCC) of 0.63 on DISFA, which is 7% higher than the performance of OpenFace 2.0 while maintaining highly-efficient inference that runs two times faster than OpenFace 2.0. Despite being compact, our model also demonstrates competitive performance to state-of-the-art facial expression analysis methods on AffecNet, FFHQ, and RAF-DB. Our code will be released at https://github.com/ihp-lab/LibreFace},

note = {arXiv:2308.10713 [cs]},

keywords = {DTIC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}

@misc{yin_fg-net_2023,

title = {FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features},

author = {Yufeng Yin and Di Chang and Guoxian Song and Shen Sang and Tiancheng Zhi and Jing Liu and Linjie Luo and Mohammad Soleymani},

url = {http://arxiv.org/abs/2308.12380},

year  = {2023},

date = {2023-08-01},

urldate = {2024-02-21},

publisher = {arXiv},

abstract = {Automatic detection of facial Action Units (AUs) allows for objective facial expression analysis. Due to the high cost of AU labeling and the limited size of existing benchmarks, previous AU detection methods tend to overfit the dataset, resulting in a significant performance loss when evaluated across corpora. To address this problem, we propose FG-Net for generalizable facial action unit detection. Specifically, FG-Net extracts feature maps from a StyleGAN2 model pre-trained on a large and diverse face image dataset. Then, these features are used to detect AUs with a Pyramid CNN Interpreter, making the training efficient and capturing essential local features. The proposed FG-Net achieves a strong generalization ability for heatmap-based AU detection thanks to the generalizable and semantic-rich features extracted from the pre-trained generative model. Extensive experiments are conducted to evaluate within- and cross-corpus AU detection with the widely-used DISFA and BP4D datasets. Compared with the state-of-the-art, the proposed method achieves superior cross-domain performance while maintaining competitive within-domain performance. In addition, FG-Net is data-efficient and achieves competitive performance even when trained on 1000 samples. Our code will be released at textbackslashurlhttps://github.com/ihp-lab/FG-Net},

note = {arXiv:2308.12380 [cs]},

keywords = {DTIC, Virtual Humans},

pubstate = {published},

tppubtype = {misc}

}