Density plots effectively summarize large numbers of points, which would otherwise lead to severe overplotting in, for example, a scatter plot. However, when applied to line-based datasets, such as trajectories or time series, density plots alone are insufficient, as they disrupt path continuity, obscuring smooth trends and rare anomalies. We propose a bin-based illumination model that decouples structure from density to enhance flow and reveal sparse outliers while preserving the original colormap. We introduce a bin-based outlierness metric to rank trajectories. Guided by this ranking, we construct a structural normal map and apply locally-adaptive lighting in the luminance channel to highlight chosen patterns-from dominant trends to atypical paths-with acceptable color distortion. Our interactive method enables analysts to prioritize main trends, focus on outliers, or strike a balance between the two. We demonstrate our method on several real-world datasets, showing it reveals details missed by simpler alternatives, achieves significantly lower CIEDE2000 color distortion than standard shading, and supports interactive updates for up to 10,000 lines.

We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more efficiently encode low-dimensional manifold geometry. Central to this approach are maximum manifold capacity representations (MMCRs), which help untangle complex manifolds by compressing variances among locally similar data points while amplifying variance among dissimilar data points. This design is particularly effective for high-dimensional data with substantial intra-cluster variance and curved manifold structures, such as biological or image data. Our qualitative and quantitative evaluations demonstrate that MAPLE can produce clearer visual cluster separations and finer subcluster resolution than UMAP while maintaining a tractable computational cost.

Finding a particular object in a display is important for viewers in many visualizations, for example, when reacting to brushing or to a highlighted object. This can be enabled by making the target object different in one of the visual variables that determine the object's appearance; for example, by changing its color or size. Certain interpretations of the visual search literature have promoted the view that using visual variables such as hue-often labeled as preattentive-would make the target object automatically "popout," implying that an object can be located almost instantly, regardless of the number of objects in the display. In this paper we present a study that serves as a bridge between the extensive visual search literature and visualization, establishing empirical base measurements for the localization task. By testing displays with up to hundreds of objects, we are able to show that none of the common visual variables is immune to the increase in the number of objects. We also provide the first empirically informed comparisons between visual variables for this task in the context of visualization, and show how different visual variables have varying robustness with respect to two additional dimensions: the location of the target and the overall visual arrangement (layout). A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/z68ak/overview.

Visual cluster analysis is a central task to explore multidimensional data. Dimensionality Reduction (DR) techniques support this task by spatializing multidimensional (MD) data similarities as point patterns in scatterplots. However, unavoidable false and missing neighbor distortions limit their accuracy. For instance, false neighbors make truly separated data clusters appear to overlap in the layout, while missing neighbors split true clusters into falsely separated groups. In general, both types of distortions exist in DR layouts except for orthogonal linear projections (OLP) that only generate false neighbors. In this work, we propose Isometric Seriation-based Dimensionality Reductions (ISilDR) that provably generate at most missing neighbors. We study how ISilDR and OLP together could be leveraged to discover true MD clusters. An ISilDR first creates a seriation of the MD data points, i.e., an ordering along a one-dimensional projection axis, and then each pair of consecutive points along this axis is spaced by their MD distance. An mD ISilDR can be obtained by combining m 1D ISilDRs. We study the theoretical and empirical characteristics of different variants of ISilDRs and OLPs and propose a systematic and formal analysis based on E-neighborhood graphs. From there, we derive rules to discover cluster patterns in MD data from interactive linking of ISilDR and OLP coordinated layouts. We then conduct case studies and illustrate scenarios for trustworthy visual cluster analysis using a combination of ISilDR and other classical DR techniques.

There are many problem domains where users have to pay attention to a series of targets. In this paper, we evaluated visual cues to support attention in dynamic situations in virtual environments. Three immersive visualizations (Emphasis, Deemphasis, Emphasis+Deemphasis) have been examined across four different attention types (sustained, alternating, divided, and selective attention) to support the user's attention on targets. Emphasis highlights targets of interest using outlines; Deemphasis removes details with masks, and Emphasis+Deemphasis combines both techniques. An experiment with 34 participants examined the cues through four scenarios that required different attention types in dynamic situations. Results indicated that while Emphasis supported significantly faster reactions in most attention types, the other two visual cues showed superiority across other measurements. Although evaluated in a virtual environment, the findings offer guidance for designing visual cues in other dynamic situations that involve different attention types.

3D Gaussian Splatting (3DGS) has attracted considerable attention due to its remarkable rendering efficiency and superior visual quality. However, typical implementations struggle with ultra-high-resolution imagery due to excessive GPU memory demands, a critical limitation particularly in domains such as aerial mapping and remote sensing, where fine-grained details are crucial for accurate 3D reconstruction. In this paper, we introduce InfiniGS, an enhanced Gaussian Splatting framework that scales up training image resolution while maintaining fidelity and efficiency, enabling reconstruction from ultra-high-resolution images without encountering out-of-memory (OOM) issues. Our framework consists of two modules; firstly, we propose a Multi-Resolution Training strategy that guides the optimization from recovering global structures toward fine-grained details, while mitigating erosion artifacts during resolution transitions. Secondly, we introduce an Image Block Partitioning and Asynchronous Optimization scheme, which significantly reduces GPU memory overhead. Most importantly, we reveal the scaling rule for the densification threshold of Gaussian primitives and the hyperparameters of the Adam optimizer during resolution transitions and image partitioning. This ensures consistently high rendering quality regardless of the multi-resolution factor or partitioning strategy employed. Extensive experiments demonstrate that InfiniGS achieves superior Novel View Synthesis (NVS) for high-resolution scenes, while requiring less memory and shorter training time. To the best of our knowledge, InfiniGS is the first method capable of reconstructing scenes with resolutions up to 10K and beyond, at full resolution and high fidelity, without exceeding the memory capacity of mainstream powerful GPUs (e.g., NVIDIA RTX 3090 with 24GB VRAM).

We propose a novel framework for Russian Roulette and Splitting (RRS) tailored to wavefront path tracing, a highly parallel rendering architecture that processes path states in batched, stage-wise execution for efficient GPU utilization. Traditional RRS methods, with unpredictable path counts, are fundamentally incompatible with wavefront's preallocated memory and scheduling requirements. To resolve this, we introduce a normalized RRS formulation with a bounded path count, enabling stable and memory-efficient execution. Furthermore, we pioneer the use of neural networks to learn RRS factors, presenting two models: NRRS and AID-NRRS. At a high level, both feature a carefully designed RRSNet that explicitly incorporates RRS normalization, with only subtle differences in their implementation. To balance computational cost and inference accuracy, we introduce Mix-Depth, a path-depth-aware mechanism that adaptively regulates neural evaluation, further improving efficiency. Extensive experiments demonstrate that our method outperforms traditional heuristics and recent RRS techniques in both rendering quality and performance across a variety of complex scenes.

Intuitive motion control is essential for virtual reality, allowing users to manipulate objects naturally while receiving realistic and responsive visual feedback. 3D Gaussian splatting provides real-time, photorealistic scene rendering, making it promising for virtual reality applications. Still, it falls short in accurate motion control of dynamic objects due to its unstructured global motion representation and redundant motion learning. To address these problems, we propose a motion hierarchical Gaussian based dynamic control method. First, a motion hierarchical Gaussian representation is introduced and initialized with semantic and deformation information. Then a motion hierarchical decomposition method is proposed to optimize the local motion in the representation. The representation is next optimized by a local motion analysis based refinement method. We also design a set of motion control operations for the motion hierarchical Gaussian. Experimental results show that our method achieves high-precision motion reconstruction, accurate motion decomposition, real-time, intuitively and immersive VR motion control.

Aim assistance has been shown to be effective in traditional video games; however, its application in VR contexts remains relatively underexplored. We investigate the effects of invisible bullet-magnetism aim assistance applied at the time of throw on performance and user experience in a VR dart-throwing task. We corrected the trajectory by adjusting the initial throwing direction to reduce the landing position deviation from the target center by 50%. We conducted a within-subjects experiment with 29 participants, comparing the aim-assist condition with a control condition without assistance. Aim assistance with invisible bullet magnetism significantly improved objective and perceived performance, accuracy, and precision. Participants in the aim-assist condition reported significantly higher perceived competence and control, while perceived suspense and pressure were scored significantly lower. Enjoyment, internal attribution of achievement, and workload remained comparable between the two conditions. Participants attributed their success to themselves rather than external factors in both conditions. The invisible bullet-magnetism aim assistance in our study remained largely unnoticed, and participants favored the aim-assist condition.

Music in virtual environments has long been treated as a temporal evolving element, enhancing atmosphere and game pace but rarely considered as a performance adaptive element. Recent advances in artificial intelligence (AI) and procedural audio make it possible to generate music that adapts in real time to player actions and system state. Yet, despite its potential, the behavioural impact of such adaptive generative soundtracks in head-mounted display-based virtual reality (VR) remains largely unexplored. To address this gap, we introduce a VR archery system that integrates Google MusicFXDJ with Ubiq-Genie to deliver continuous AI-generated adaptive music driven by gameplay events. In a within-subjects experiment (N = 22), participants completed trials with either a stylistically-matched fixed soundtrack or an adaptive soundtrack that escalated tension across four phases as arrows depleted. Measures combined self-reported ratings of presence, focus, stress, and emotional impact, with performance metrics of accuracy and aiming time. Results reveal that adaptive generative music not only heightens immersion and emotional salience but also modulates motor precision in an arousal-dependent inverted-U pattern: moderate musical tension improved accuracy and speed, whereas excessive tension impaired them. These findings establish AI-generated music as a powerful behavioural feedback modality in VR, opening pathways for training, rehabilitation, and next-generation immersive entertainment.