Generating high-quality physically based rendering (PBR) materials is important to achieve realistic rendering in the downstream tasks, yet it remains challenging due to the intertwined effects of materials and lighting. While existing methods have made breakthroughs by incorporating material decomposition in the 3D generation pipeline, they tend to bake highlights into albedo and ignore spatially varying properties of metallicity and roughness. In this work, we present PBR3DGen, a two-stage mesh generation method with high-quality PBR materials that integrates the novel multi-view PBR material estimation model and a 3D PBR mesh reconstruction model. Specifically, PBR3DGen leverages vision language models (VLM) to guide multi-view diffusion, precisely capturing the spatial distribution and inherent attributes of reflective-metalness material. Additionally, we incorporate view-dependent illumination-aware conditions as pixel-aware priors to enhance spatially varying material properties. Furthermore, our reconstruction model reconstructs high-quality mesh with PBR materials. Experimental results demonstrate that PBR3DGen significantly outperforms existing methods, achieving new state-of-the-art results for PBR estimation and mesh generation.

In interpersonal interactions, individuals often exhibit avoidance behaviors toward others they find unpleasant, which can undermine the comfort of everyday social experiences. Existing human-computer interaction (HCI) research has primarily focused on promoting social connections, while support for avoidance-oriented social situations remains underexplored. To address this gap, we propose leveraging Diminished Reality (DR) technology to obscure perceptual cues of undesirable individuals. We designed and implemented a mixed reality prototype system and conducted experiments manipulating both the occlusion method and social distance. Results indicate that DR significantly reduces users’ social anxiety and sense of social presence. Moreover, participants generally expressed positive attitudes toward usage intention and ethical considerations. This work extends HCI research on social comfort, shifting the focus from “facilitating connection” to “supporting avoidance”.

Aggressive driving often triggers anger and retaliatory behaviors, posing threats to traffic safety. This paper proposes an AI-driven apology mechanism based on an Augmented Reality Head-Up Display (AR-HUD), which delivers immediate apologies on behalf of offending drivers during traffic conflicts and repairs damaged social relations through prosocial lies. We conducted a 2 (scenario risk: high vs. low) × 5 (apology depth) mixed-design experiment (N = 40) to evaluate its effectiveness. Results show that AI apologies enhanced positive emotions and forgiveness intentions while reducing anger, with participants also perceiving psychological benefits. These effects were consistent across both high- and low-risk scenarios. Our findings offer a practical design pathway for human-AI emotional regulation in traffic contexts.

The perception of trustworthiness in artificial faces is crucial for enhancing user experiences in virtual environments. However, research on the specific factors influencing this perception is limited. Therefore, this study aimed to discover the factors that affect people’s judgment of facial trustworthiness in the virtual world. Twenty-five documents were retrieved from databases, including PsycArticles, PsycInfo, Scopus, the IEEE Xplore digital library, and Web of Science, through a systematic review. Followed by a content analysis, the findings reveal that face features (internal and external), age, gender, participant ethnicity, and experimental measurement methods all shape perceptions of trustworthiness in artificial faces. Based on the findings, the study highlights opportunities for enhancing the perception of trustworthiness in virtual settings, which could provide valuable insights for researchers and highlight critical areas for future exploration in the design of artificial faces, participant selection, and measurement techniques.

Realistic simulation of dynamic scenes requires accurately capturing diverse material properties and modeling complex object interactions grounded in physical principles. However, existing methods are constrained to basic material types with limited predictable parameters, making them insufficient to represent the complexity of real-world materials. We introduce PhysFlow, a novel approach that leverages multi-modal foundation models and video diffusion to achieve enhanced 4D dynamic scene simulation. Our method utilizes multi-modal models to identify material types and initialize material parameters through image queries, while simultaneously inferring 3D Gaussian splats for detailed scene representation. We further refine these material parameters using video diffusion with a differentiable Material Point Method (MPM) and optical flow guidance rather than render loss or Score Distillation Sampling (SDS) loss. This integrated framework enables accurate prediction and realistic simulation of dynamic interactions in real-world scenarios, advancing both accuracy and flexibility in physics-based simulations. Our code and data are available at https://zhuomanliu.github.io/PhysFlow

3D inverse rendering in indoor scenes with strong light sources presents a significant challenge, primarily due to the substantial ambiguity in material recovery caused by the complex interaction between lighting and shadows. To address this, we propose a novel approach that integrates an implicit-explicit shadow predictor with a three-stage material estimation process. Our method enhances shadow realism by accurately predicting light interactions, while our material estimation process improves SVBRDF quality under challenging lighting conditions. Extensive experiments demonstrate the effectiveness of our method in both quantitative and qualitative metrics, enabling realistic object insertion and material replacement with proper shadow rendering under strong indoor light sources.

Visual blind spots caused by occlusion from lead vehicles represent a significant latent risk factor contributing to traffic accidents. Although Level 2 (L2) autonomous driving systems can partially mitigate this issue, human drivers are still required to actively perceive potential hazards and understand the behaviour of the autonomous vehicle to ensure driving safety. Therefore, designing interactive interfaces that reduce the risks associated with visual blind spots is critical in autonomous driving scenarios. However, current research on this typical scenario remains relatively limited. This study proposes an innovative cooperative driving warning strategy, focussing on driving situations where the driver's line of sight is blocked by a lead vehicle. The strategy integrates environmental cues and behavioural information from the autonomous driving system through an augmented reality (AR) interface, aiming to facilitate efficient cooperation between human drivers and autonomous systems in perceiving visual blind spots. We systematically evaluated the proposed interface prototype using a driving simulator under L2 autonomous driving conditions. The results indicate that the interface significantly enhances drivers' situation awareness and reduces their reaction time to potential hazards. Additionally, the design improves the quality of human-machine cooperation by decreasing conflicts with the autonomous system, increasing trust in the system, and significantly boosting user satisfaction. This study provides new insights into the design of human-machine cooperative perception interfaces for blind spot scenarios and offers both theoretical foundations and practical implications for the future development of cooperative driving systems.

Real-time goal collaboration represents a promising approach to human-vehicle cooperative driving; however, it remains underexplored. To address this gap, we introduced an innovative human-vehicle cooperative approach and designed four interactive types with increasing autonomous levels to implement it. Additionally, we proposed seven interface design principles to design three increasing levels of transparency for the four interactive types, aiming to enhance collaboration. Experimental results demonstrate the favorable reception of the proposed cooperative approach by users. Furthermore, higher interactive autonomous levels result in reduced workload, and higher interface transparency levels lead to increased satisfaction, trust, and mutual dependence. Notably, the combination of the highest interactive autonomous level and interface transparency level, which exhibited the best performance, is recommended for practical application. This collaborative approach expands the research domain of human-vehicle cooperative driving and offers extensive potential applications across various relevant scenarios.

Grasping user-specified objects is crucial for robotic assistants; however, most current 6-DoF grasp detection methods are object-agnostic, making it challenging to grasp specific targets from a scene. To achieve that, we present GoalGrasp, a simple yet effective 6-DoF robot grasp pose detection method that does not rely on grasp pose annotations and grasp training. By combining 3-D bounding boxes and simple human grasp priors, our method introduces a novel paradigm for robot grasp pose detection. GoalGrasp's novelty is its swift grasping of user-specified objects and partial mitigation of occlusion issues. The experimental evaluation involves 18 common objects categorized into 7 classes. Our method generates dense grasp poses for 1000 scenes. We compare our method's grasp poses to existing approaches using a novel stability metric, demonstrating significantly higher grasp pose stability. In user-specified robot grasping tests, our method achieves a 94% success rate, and 92% under partial occlusion.

In recent studies of 3D shape modelling and reconstruction, the focus has primarily been on the 3D face region. However, accurately creating the entire 3D head opens up a wide range of applications, including headwear design, cranial diagnosis, and avatar design. Therefore, we present our newly developed method of constructing 3D comprehensive morphable models (3DCMM) specifically tailored for human heads, along with a novel 3DCMM-based stepwise pipeline for creating accurate full 3D heads. Within our 3DCMM framework, we constructed a powerful 3D morphable face model with UV-UNet to generate the 3D face and predict the 3D scalp, resulting in a complete representation of the head. Additionally, our 3DCMM-based self-learning approach incorporates novel facial boundary-aware and structure-aware losses for highly accurate overall reconstructions of the entire facial region. Experimental evaluations demonstrate that our 3DCMM exhibits superior face representation power and achieves higher head prediction accuracy than existing models. Consequently, our 3DCMM-based 3D head creation method from a single image demonstrates outstanding performance capability on both face and head benchmarks.

The increasing interest surrounding 3D human heads for digital avatars and simulations has highlighted the need for accurate internal modeling rather than solely focusing on external approximations. Existing approaches rely on traditional optimization techniques applied to explicit 3D representations like point clouds and meshes, leading to computational inefficiencies and challenges in capturing local geometric features. To tackle these problems, we propose a novel modeling method called DDF-ISM. It leverages a probabilistic Directed Distance Field for Internal Structure Modeling, facilitating efficient and anatomically accurate deformation of different parts of the human head. DDF-ISM comprises two key components: 1) a probabilistic DDF network for implicit representation of the target model to provide crucial local geometric information, and 2) a conditioned deformation network guided by the local geometry. Additionally, we introduce a large-scale dataset of human heads with internal structures derived from high-quality Computed Tomography (CT) scans, along with well-designed template models encompassing skull, mandible, brain, and head surface. Evaluation on this dataset showcases the superiority of our approach over existing methods, exhibiting superior performance in both modeling quality and efficiency.

Natural motion mapping enhances the gaming experience by reducing the cognitive burden and increasing immersion. However, many players still use the keyboard and mouse in recent commercial PC games. To solve the conflict between complex avatar motion and the limited interaction system, we introduced CamTroller, an auxiliary tool for commercial one-to-one avatar mapping PC games following the concept of a NUI (natural user interface). To validate this concept, we selected PUBG as the application scenario and developed a proof-of-concept system to help players achieve a better experience by naturally mapping selected human motions to the avatars in games through an RGB webcam. A within-subject study with 18 non-professional players practiced common operation (Basic), professional player’s operation (Pro), and CamTroller. Results showed that the performance of CamTroller was as good as the Pro and significantly higher than Basic. Also, the subjective evaluation showed that CamTroller achieved significantly higher intuitiveness than Basic and Pro.

As the metaverse develops rapidly, 3D facial age transformation is attracting increasing attention, which may bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we propose a new mesh-to-mesh Wasserstein generative adversarial network (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process. To the best of our knowledge, this is the first architecture to achieve 3D facial geometric age transformation via real 3D scans. As previous image-to-image translation methods cannot be directly applied to the 3D facial mesh, which is totally different from 2D images, we built a mesh encoder, decoder, and multi-task discriminator to facilitate mesh-to-mesh transformations. To mitigate the lack of 3D datasets containing children's faces, we collected scans from 765 subjects aged 5-17 in combination with existing 3D face databases, which provided a large training dataset. Experiments have shown that our architecture can predict 3D facial aging geometries with better identity preservation and age closeness compared to 3D trivial baselines. We also demonstrated the advantages of our approach via various 3D face-related graphics applications.

In this paper, we study the problem of continuous 3D shape representations. The majority of existing successful methods are coordinate-based implicit neural representations. However, they are inefficient to render novel views or recover explicit surface points. A few works start to formulate 3D shapes as ray-based neural functions, but the learned structures are inferior due to the lack of multi-view geometry consistency. To tackle these challenges, we propose a new framework called RayDF. It consists of three major components: 1) the simple ray-surface distance field, 2) the novel dual-ray visibility classifier, and 3) a multi-view consistency optimization module to drive the learned ray-surface distances to be multi-view geometry consistent. We extensively evaluate our method on three public datasets, demonstrating remarkable performance in 3D surface point reconstruction on both synthetic and challenging real-world 3D scenes, clearly surpassing existing coordinate-based and ray-based baselines. Most notably, our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image, showing the superiority of our method for 3D shape representation. Our code and data are available at https://github.com/vLAR-group/RayDF

Sleep stage classification is a critical concern in sleep quality assessment and disease diagnosis. Graph network based studies for sleep stages classification have achieved promising performance. However, these studies still ignored the importance of learning morphological feature information with the spatial-temporal relationship among multi-modal physiological signals. To address this issue, we propose a Spatial-temporal Adaptive Graph Network named StAGN for sleep stage classification. The main advantage of StAGN is to adaptively learn the time-dependent and channel-wise interdependent waveform morphological features in multimodal physiological signals. Such features will be extracted by a modified 1-dimensional ResNet with a projection shortcut connection and adjusted by a joint spatial-temporal attention, thereby best serving the followed brain topological connection graph network for sleep stage classification. Meanwhile, we leverage the contrastive learning scheme with label information to further improve classification accuracy without changing the signal morphology. Experiment results on two publicly available sleep datasets of ISRUC-S1 and ISRUC-S3 show that the proposed StAGN can achieve a competitive performance for sleep stage classification, which is superior to the state-of-the-art counterparts.

The interface metaphor was suggested to facilitate a user’s mental model development while navigating, whereas its effectiveness is still unknown among the group of older adults. We developed an experiment to investigate how older adults navigate the mobile interfaces with and without metaphors, by evaluating the possible effects of users’ perceptual speed, task complexity, content similarity, and other related user characteristics in an integrated fashion. Results indicated that the use of interface metaphor could assist in the older adults’ navigation performance, but only for those with a higher level of perceptual speed. Additionally, we found that the older adult’s navigation behavior was significantly influenced by the task complexity, content similarity and the users’ level of technology experience. These findings can help researchers and practitioners to evaluate the effectiveness of interface metaphor on older adults’ mobile navigation behavior by identifying the possible influential factors and interpreting the behind reasons.

While mobile payment services have been flourishing in China, users have continually questioned the security of these transactions. Although customization has been proposed as a vital factor for mobile commerce, minimal knowledge exists regarding how it affects users’ perceived security in mobile payment transactions. A quantitative diary study was therefore conducted to provide insight into the personality traits that motivate customization behaviors in security, and how such behaviors influence perceived security under different use contexts in relation to mobile payments. First, an instrument for the diary study was developed through an interview. Then, 134 responses from mobile payment users were used to examine the relationships between personality traits and customization behaviors. Among them, the diary was completed by 67 mobile payment users who reported their perceived security for 1094 recoded payment events across various use contexts for periods ranging between 5 and 15 days. The results showed that the personality traits of extraversion and intellect influence users’ customization behaviors and these behaviors have a positive effect on perceived security. Additionally, the relationship between customization behaviors and perceived security was moderated by the task and technical contexts. Based on these findings, design implications and opportunities for mobile payment services are described.

Mobile devices are becoming ubiquitous among older adults, but have also caused unprecedented challenges due to the high demands of interaction techniques and changeable design patterns found across various applications. This paper aims to investigate how older adults navigate with mobile interfaces and identify their potential usability challenges while navigating. To do so, we summarised six state-of-the-art mobile interface design patterns and conducted individual usability test and in-depth interview with 22 older adults. Participants were asked to perform 19 navigation tasks that contain these design patterns under realistic usage scenarios. Follow-up interviews were held to collect their detailed comments on usability issues regarding visual design, ease of understanding, and interaction and navigation of the design patterns, as well as their personal experience. The results found that overall older adults were able to navigate contents more effectively than menus and buttons. Participants experienced great challenges in directing their attention to the menus and buttons, understanding the meaning of icons, and interacting with these menu components. In contrast, the content-oriented navigation design performed better in understanding, navigation, and interaction, which could be a promising direction for elderly-friendly mobile application design. Design implications are further discussed for creating an elderly-friendly mobile interface.

With the increasing complexity of control rooms and the information explosion, effective multitasking is now desired. Monochronicity and polychronicity, which describe a person's ability to do one thing and many things at a time, respectively, have been studied for a long time. However, it is not clear these abilities are related to various individual characteristics. Forty-eight Chinese participants were tested on their perception, memory, judgement, attention ability and cognitive style. They also performed a task that required search and calculation under three conditions of unpaced, paced and paced with sequencing. There were significant differences in the performance and strategy between monochronic and polychronic individuals in the selective attention test. Monochronic individuals focused their attention on the primary task and achieved higher performance. Polychronic individuals had somewhat better total performance in more than one task under time-constrained conditions. The results clearly indicate that an individual's time use behaviours ought to be considered in training and control scenarios to account for differences among people.