Kunal Bhosikar, Siddharth Katageri, Vivek Madhavaram
et al.
Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack the fidelity for fine-grained tasks like object grasping. Conversely, methods that generate precise grasping motions typically ignore the surrounding 3D scene. This gap, generating full-body grasping motions that are physically plausible within a 3D scene, remains a significant challenge. To address this, we introduce MOGRAS (Human MOtion with GRAsping in 3D Scenes), a large-scale dataset that bridges this gap. MOGRAS provides pre-grasping full-body walking motions and final grasping poses within richly annotated 3D indoor scenes. We leverage MOGRAS to benchmark existing full-body grasping methods and demonstrate their limitations in scene-aware generation. Furthermore, we propose a simple yet effective method to adapt existing approaches to work seamlessly within 3D scenes. Through extensive quantitative and qualitative experiments, we validate the effectiveness of our dataset and highlight the significant improvements our proposed method achieves, paving the way for more realistic human-scene interactions.
Frederic Gmeiner, Nicolai Marquardt, Michael Bentley
et al.
Despite Generative AI (GenAI) systems' potential for enhancing content creation, users often struggle to effectively integrate GenAI into their creative workflows. Core challenges include misalignment of AI-generated content with user intentions (intent elicitation and alignment), user uncertainty around how to best communicate their intents to the AI system (prompt formulation), and insufficient flexibility of AI systems to support diverse creative workflows (workflow flexibility). Motivated by these challenges, we created IntentTagger: a system for slide creation based on the notion of Intent Tags - small, atomic conceptual units that encapsulate user intent - for exploring granular and non-linear micro-prompting interactions for Human-GenAI co-creation workflows. Our user study with 12 participants provides insights into the value of flexibly expressing intent across varying levels of ambiguity, meta-intent elicitation, and the benefits and challenges of intent tag-driven workflows. We conclude by discussing the broader implications of our findings and design considerations for GenAI-supported content creation workflows.
Retrieval-Augmented Generation (RAG) systems offer a powerful approach to enhancing large language model (LLM) outputs by incorporating fact-checked, contextually relevant information. However, fairness and reliability concerns persist, as hallucinations can emerge at both the retrieval and generation stages, affecting users' reasoning and decision-making. Our research explores how tailored warning messages -- whose content depends on the specific context of hallucination -- shape user reasoning and actions in an educational quiz setting. Preliminary findings suggest that while warnings improve accuracy and awareness of high-level hallucinations, they may also introduce cognitive friction, leading to confusion and diminished trust in the system. By examining these interactions, this work contributes to the broader goal of AI-augmented reasoning: developing systems that actively support human reflection, critical thinking, and informed decision-making rather than passive information consumption.
Oliver Chojnowski, Alexander Eberhard, Michael Schiffmann
et al.
Socially interactive agents are gaining prominence in domains like healthcare, education, and service contexts, particularly virtual agents due to their inherent scalability. To facilitate authentic interactions, these systems require verbal and nonverbal communication through e.g., facial expressions and gestures. While natural language processing technologies have rapidly advanced, incorporating human-like nonverbal behavior into real-world interaction contexts is crucial for enhancing the success of communication, yet this area remains underexplored. One barrier is creating autonomous systems with sophisticated conversational abilities that integrate human-like nonverbal behavior. This paper presents a distributed architecture using Epic Games MetaHuman, combined with advanced conversational AI and camera-based user management, that supports methods like motion capture, handcrafted animation, and generative approaches for nonverbal behavior. We share insights into a system architecture designed to investigate nonverbal behavior in socially interactive agents, deployed in a three-week field study in the Deutsches Museum Bonn, showcasing its potential in realistic nonverbal behavior research.
Tauhid Tanjim, Jonathan St. George, Kevin Ching
et al.
The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significant progress in human-robot teaming research, there remains an essential gap in how robots can effectively communicate with action teams using multimodal interaction cues in time-sensitive environments. This study addresses this knowledge gap in an experimental in-lab study to investigate how multimodal robot communication in action teams affects workload and human perception of robots. We explore team collaboration in a medical training scenario where a robotic crash cart (RCC) provides verbal and non-verbal cues to help users remember to perform iterative tasks and search for supplies. Our findings show that verbal cues for object search tasks and visual cues for task reminders reduce team workload and increase perceived ease of use and perceived usefulness more effectively than a robot with no feedback. Our work contributes to multimodal interaction research in the HRI field, highlighting the need for more human-robot teaming research to understand best practices for integrating collaborative robots in time-sensitive environments such as in hospitals, search and rescue, and manufacturing applications.
Urbanisation across sub-Saharan Africa is transforming the spatial structure of secondary towns, often generating uneven and fragmented growth. A key manifestation of this process is intra-urban dualism, where well-planned, affluent neighbourhoods coexist with densely populated, poorly regulated settlements. This spatial divide undermines orderly growth, deepens inequality, and places pressure on urban infrastructure. In Kenya, intra-urban dualism is increasingly evident, yet limited research has explored how it influences land-use transformation and sustainable development. Addressing this research gap is essential to understand how spatial inequalities shape urban growth trajectories and to guide equitable planning interventions. This study examines intra-urban dualism and land-use transformation in Kisii town, western Kenya, focusing on the contrasting neighbourhoods of Milimani (a low-density planned area) and Jogoo (a high-density unregulated settlement). Land-use and land-cover changes from 2005 to 2024 were analysed and projected to 2044, using ArcGIS Pro and QGIS. Building density, plot size compliance, and coverage ratios were quantified and validated through a one-sample t-test. Results show that Milimani has largely retained its planned form, whereas Jogoo has undergone rapid, unregulated densification driven by weak development control and fragmented land ownership. The study recommends data-driven, geospatially informed development control supported by adaptive zoning, participatory monitoring, blockchain-based permitting, and resilience audits to promote sustainable, inclusive, and transparent urban growth.
Cities. Urban geography, Urban groups. The city. Urban sociology
Abstract The notion of reciprocity between humans and the rest of the living world is receiving increasing attention in the environmental sciences and science–policy international bodies. Here we first discuss different meanings of reciprocity, then we discuss this notion in relation to the conceptual framework of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), to date the most prominent international mechanism informing the science–policy interface on living nature. We show that the notion of human–nature reciprocity is recognized and is explicitly included in the IPBES conceptual framework. However, to date, it has received comparatively little attention. To overcome this, we argue that, rather than creating new separate ad‐hoc categories that risk compromising the internal consistency and pluralism of the IPBES conceptual framework, co‐created across different disciplines, worldviews and policy frames, a more fruitful path would be to interpret all its components in a reciprocity light, with stronger emphasis on the human shaping of, and practices of care towards the rest of the living world. Such attention to reciprocity should contribute to the evolution of IPBES and related science–policy initiatives, by incorporating a plurality of perspectives, while still maintaining the framework operational by the continued engagement of multiple disciplines and stakeholders. In terms of policy and action, this would involve more attention to pre‐existing practices of care for nature—of which we provide a few illustrative examples—and new practices inspired by them or created afresh. Read the free Plain Language Summary for this article on the Journal blog.
Passive tracking methods, such as phone and wearable sensing, have become dominant in monitoring human behaviors in modern ubiquitous computing studies. While there have been significant advances in machine-learning approaches to translate periods of raw sensor data to model momentary behaviors, (e.g., physical activity recognition), there still remains a significant gap in the translation of these sensing streams into meaningful, high-level, context-aware insights that are required for various applications (e.g., summarizing an individual's daily routine). To bridge this gap, experts often need to employ a context-driven sensemaking process in real-world studies to derive insights. This process often requires manual effort and can be challenging even for experienced researchers due to the complexity of human behaviors. We conducted three rounds of user studies with 21 experts to explore solutions to address challenges with sensemaking. We follow a human-centered design process to identify needs and design, iterate, build, and evaluate Vital Insight (VI), a novel, LLM-assisted, prototype system to enable human-in-the-loop inference (sensemaking) and visualizations of multi-modal passive sensing data from smartphones and wearables. Using the prototype as a technology probe, we observe experts' interactions with it and develop an expert sensemaking model that explains how experts move between direct data representations and AI-supported inferences to explore, question, and validate insights. Through this iterative process, we also synthesize and discuss a list of design implications for the design of future AI-augmented visualization systems to better assist experts' sensemaking processes in multi-modal health sensing data.
Ammar N. Abbas, Chidera W. Amazu, Joseph Mietkiewicz
et al.
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.
Reyna Vergara González, Victor Hugo Torres-Preciado, Miguel Angel Díaz Carreño
La evaluación de la política monetaria sobre la actividad económica se ha enfocado al ámbito nacional sin considerar sus efectos a nivel regional. El objetivo de este documento es estimar los efectos que un incremento no previsto de la tasa de interés tendría sobre la producción regional en México para el periodo 2000-2019. Para ello, se emplea un modelo estructural de vectores autorregresivos (SVAR) en panel. Los resultados sugieren un efecto negativo y diferenciado sobre la producción regional. Este efecto se presenta a partir del segundo trimestre, después del aumento de la tasa de interés.
Cities. Urban geography, Urban groups. The city. Urban sociology
Automated decision-making systems are becoming increasingly ubiquitous, which creates an immediate need for their interpretability and explainability. However, it remains unclear whether users know what insights an explanation offers and, more importantly, what information it lacks. To answer this question we conducted an online study with 200 participants, which allowed us to assess explainees' ability to realise explicated information -- i.e., factual insights conveyed by an explanation -- and unspecified information -- i.e, insights that are not communicated by an explanation -- across four representative explanation types: model architecture, decision surface visualisation, counterfactual explainability and feature importance. Our findings uncover that highly comprehensible explanations, e.g., feature importance and decision surface visualisation, are exceptionally susceptible to misinterpretation since users tend to infer spurious information that is outside of the scope of these explanations. Additionally, while the users gauge their confidence accurately with respect to the information explicated by these explanations, they tend to be overconfident when misinterpreting the explanations. Our work demonstrates that human comprehension can be a double-edged sword since highly accessible explanations may convince users of their truthfulness while possibly leading to various misinterpretations at the same time. Machine learning explanations should therefore carefully navigate the complex relation between their full scope and limitations to maximise understanding and curb misinterpretation.
This paper presents a novel free-hand sketch synthesis approach addressing explicit abstraction control in class-conditional and photo-to-sketch synthesis. Abstraction is a vital aspect of sketches, as it defines the fundamental distinction between a sketch and an image. Previous works relied on implicit control to achieve different levels of abstraction, leading to inaccurate control and synthesized sketches deviating from human sketches. To resolve this challenge, we propose two novel abstraction control mechanisms, state embeddings and the stroke token, integrated into a transformer-based latent diffusion model (LDM). These mechanisms explicitly provide the required amount of points or strokes to the model, enabling accurate point-level and stroke-level control in synthesized sketches while preserving recognizability. Outperforming state-of-the-art approaches, our method effectively generates diverse, non-rigid and human-like sketches. The proposed approach enables coherent sketch synthesis and excels in representing human habits with desired abstraction levels, highlighting the potential of sketch synthesis for real-world applications.
Yi-Shiuan Tung, Matthew B. Luebbers, Alessandro Roncone
et al.
Understanding the intentions of human teammates is critical for safe and effective human-robot interaction. The canonical approach for human-aware robot motion planning is to first predict the human's goal or path, and then construct a robot plan that avoids collision with the human. This method can generate unsafe interactions if the human model and subsequent predictions are inaccurate. In this work, we present an algorithmic approach for both arranging the configuration of objects in a shared human-robot workspace, and projecting ``virtual obstacles'' in augmented reality, optimizing for legibility in a given task. These changes to the workspace result in more legible human behavior, improving robot predictions of human goals, thereby improving task fluency and safety. To evaluate our approach, we propose two user studies involving a collaborative tabletop task with a manipulator robot, and a warehouse navigation task with a mobile robot.
Large language models (LLMs) are increasingly capable and prevalent, and can be used to produce creative content. The quality of content is influenced by the prompt used, with more specific prompts that incorporate examples generally producing better results. On from this, it could be seen that using instructions written for crowdsourcing tasks (that are specific and include examples to guide workers) could prove effective LLM prompts. To explore this, we used a previous crowdsourcing pipeline that gave examples to people to help them generate a collectively diverse corpus of motivational messages. We then used this same pipeline to generate messages using GPT-4, and compared the collective diversity of messages from: (1) crowd-writers, (2) GPT-4 using the pipeline, and (3 & 4) two baseline GPT-4 prompts. We found that the LLM prompts using the crowdsourcing pipeline caused GPT-4 to produce more diverse messages than the two baseline prompts. We also discuss implications from messages generated by both human writers and LLMs.
We propose a bootstrapping framework to enhance human optical flow and pose. We show that, for videos involving humans in scenes, we can improve both the optical flow and the pose estimation quality of humans by considering the two tasks at the same time. We enhance optical flow estimates by fine-tuning them to fit the human pose estimates and vice versa. In more detail, we optimize the pose and optical flow networks to, at inference time, agree with each other. We show that this results in state-of-the-art results on the Human 3.6M and 3D Poses in the Wild datasets, as well as a human-related subset of the Sintel dataset, both in terms of pose estimation accuracy and the optical flow accuracy at human joint locations. Code available at https://github.com/ubc-vision/bootstrapping-human-optical-flow-and-pose
<p>With this contribution (which is designed as a positioning), the author
pleads for a more consistent consideration of structural influences in the
discussion of mortality in (textbook) population geography, and for a
critical discussion of these influences. He refers to various conceptions
that already have fixed places in human geography – but not in population
geography – and that offer starting points for corresponding discussions.</p>
Human ecology. Anthropogeography, Geography (General)
Juanita Aldana-Domínguez, Ignacio Palomo, Julian Arellana
et al.
Rapid urbanization trends and urban lifestyles challenge urban populations to recognize ecosystems’ contributions to their well-being, and urban planners to integrate nature at the core of urban development. This study assesses the relationships between ecosystems and people in the rapidly expanding Barranquilla Metropolitan Area (BMA) and extracts lessons for its planning as a BiodiverCity. Using 22 interviews and 400 face-to-face surveys we evaluated: 1) the perception of positive and negative contributions of specific types of ecosystems to human well-being (HWB); 2) the importance and vulnerability of multiple ecosystem services (ES) and disservices (EdS); and 3) the relationships between ES, EdS and relational values (RV), and the influence of socioeconomic factors in providing HWB, using a Structural Equation Model (SEM). Open-ended answers in the survey showed that rural and certain natural ecosystems, such as wetlands, mangroves and tropical dry forest were the least valued ecosystems and included some EdS. In contrast, urban and peri-urban ecosystems, namely the river, beaches, crops, urban green, and backyards, were the most valued. Overall, regulating ES were perceived as critical, as well as important and vulnerable. The results of the SEM model indicate that HWB is not only explained by socioeconomic factors such as income and education, but also by ES. We argue that the necessary sustainable socio-economic development of the BMA should be coupled with an urban planning that integrates ES and their contributions to HWB.
Human ecology. Anthropogeography, Environmental sciences