Interactive Modeling of Trees Using VR Devices

The ability to specify nonplanar 3D curves is of fundamental importance in 3D modeling and animation systems. Effective techniques for specifying such curves using 2D input devices are desirable, but existing methods typically require the user to edit the curve from several viewpoints. We present a novel method for specifying 3D curves with 2D input from a single viewpoint. The user rst draws the curve as it appears from the current viewpoint, and then draws its shadow on the oor plane. The system correlates the curve with its shadow to compute the curve's 3D shape. This method is more natural than existing methods in that it leverages skills that many artists and designers have developed from work with pencil and paper.

2D tree object representation via the slope chain code

The Smart House Planner is an AI-powered solution designed to simplify the process of residential home design by combining intelligent planning, 3D modelling, and real-time cost estimation through an interactive conversational interface. With the rising demand for personalized housing solutions and the complexity of balancing aesthetics, functionality, and budget, there is a growing need for systems that can automate architectural planning while remaining user-friendly. This project addresses that challenge by integrating Gemini AI for intelligent design recommendations, Blender for automated 3D visualization, and a cost estimation engine for transparent financial planning. The system enables users to specify requirements such as housing type (1BHK, 2BHK, 3BHK, Duplex, Villa), dimensions, and style preferences via a modern Django-based web interface. Gemini AI processes these inputs to generate optimized layouts by considering space utilization, and functional flow. These layouts are then transformed into realistic, interactive 3D models using Blender, allowing users to explore their prospective homes virtually. Simultaneously, the cost estimation module calculates material, labour, costs, producing downloadable reports that support informed decision-making and budget control. By unifying AI-driven design intelligence, real-time 3D modelling, and detailed cost breakdowns, Smart House Planner provides a comprehensive platform that enhances user engagement, reduces reliance on professional intervention, and democratizes access to architectural planning. This solution demonstrates how AI, visualization technologies, and web-based interfaces can transform the way individuals approach housing design, making the process more efficient, transparent, and accessible to both professionals and non-experts.

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive the best reward and take actions according to 3D hand gestures input. The UAV consist of a Jetson Nano embedded testbed, Global Positioning System (GPS) sensor module, and Intel depth camera. The collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward function. The analysis of the results has been observed providing best accuracy and computational time using novel design framework when compared with traditional Proportional Integral Derivatives (PID) flight controller. There are six reward functions estimated for 2500, 5000, 7500, and 10000 episodes of training, which have been normalized between 0 to −4000. The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value. The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%.

In this paper, we present a prototype for exploring natural gesture interaction with Handheld Augmented Reality (HAR) applications, using visual tracking based AR and freehand gesture based interaction detected by a depth camera. We evaluated this prototype in a user study comparing 3D gesture input methods with traditional touch-based techniques, using canonical manipulation tasks that are common in AR scenarios. We collected task performance data and user feedback via a usability questionnaire. The 3D gesture input methods were found to be slower, but the majority of the participants preferred them and gave them higher usability ratings. Being intuitive and natural was the most common feedback about the 3D freehand interface. We discuss implications of this research and directions for further work.

Interactive 3D modeling in VR is both aided by immersive 3D input and hampered by model disjunct, tool-based or selection-action user interfaces. We propose a direct, signifier-based approach to the popular interactive technique of creating 3D models through a sequence of extrusion operations. Motivated by handles and signifiers that communicate the affordances of everyday objects, we define a set of design principles for an immersive, signifier-based modeling interface. We then present an interactive 3D modeling system where all modeling affordances are modelessly reachable and signified on the model itself.

ObjectivesThe need for remote delivery of mental health interventions including instruction in meditation has become paramount in the wake of the current global pandemic. However, the support one may usually feel within the physical presence of an instructor may be weakened when interventions are delivered remotely, potentially impacting one’s meditative experiences. Use of head-mounted displays (HMD) to display video-recorded instruction may increase one’s sense of psychological presence with the instructor as compared to presentation via regular flatscreen (e.g., laptop) monitor. This research therefore evaluated a didactic, trauma-informed care approach to instruction in mindfulness meditation by comparing meditative responses to an instructor-guided meditation when delivered face-to-face vs. by pre-recorded 360° videos viewed either on a standard flatscreen monitor (2D format) or via HMD (i.e., virtual reality [VR] headset; 3D format).MethodsYoung adults (n = 82) were recruited from a university introductory course and experienced a 360° video-guided meditation via HMD (VR condition, 3D format). They were also randomly assigned to practice the same meditation either via scripted face-to-face instruction (in vivo [IV] format) or when viewed on a standard laptop display (non-VR condition, 2D format). Positive and negative affect and meditative experience ratings were self-reported and participants’ maintenance of focused attention to breathing (i.e., meditation breath attention scores [MBAS]) were recorded during each meditation.ResultsMeditating in VR (3D format) was associated with a heightened experience of awe overall. When compared to face-to-face instruction (IV format), VR meditation was rated as less embarrassing but also less enjoyable and more tiring. When compared to 2D format, VR meditations were associated with greater experiences of relaxation, less distractibility from the process of breathing, and less fatigue. No differences were found between VR and non-VR meditation in concentration (MBAS). Baseline posttraumatic stress symptoms were risk factors for experiencing distress while meditating in either (VR and non-VR) instructional format. Of those who reported a preference for one format, approximately half preferred the VR format and approximately half preferred the IV format.ConclusionsRecorded 360° video instruction in meditation viewed with a HMD (i.e., VR/3D format) appears to offer some experiential advantage over instructions given in 2D format and may offer a safe—and for some even preferred—alternative to teaching meditation face-to-face.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12671-021-01612-w.

This paper examines a new technique for the visualization of and the interaction with trees, objects frequently used to convey hierarchical relationships in biological data. Motivated by the quality of 2D tree interaction, we adapt the planar tree-of-life metaphor to a virtual, semi-immersive 3D environment. A 3D environment extends the utility of this metaphor by allowing the user to view an entire data set in a single screen. Interrogation of the tree is implemented using 3D input devices. This real-time interrogation of the tree itself provides a quick means by which to qualitatively analyse the hierarchical data. In this paper, we describe the techniques underlying the implementation of such an environment. We conclude by considering the utility of tree metaphors as a basis for the representation of highly dimensional data sets. Arbor3D (source code, a binary executable for SGI IRIX 6.4, Perl parsers, and sample Newick data files) are available via the Internet (http://xian.tamu.edu/Arbor3D/). Arbor3D can be displayed in "CAVE simulator" mode on an SGI workstation screen, or as an interactive virtual environment on a projection workbench. druths@rice.edu; echen@cs.rice.edu; leland@xian.tamu.edu

The most time-consuming part of character animation is 3D character posing. Posing using a mouse is a slow and tedious task that involves sequences of selecting on-screen control handles and manipulating the handles to adjust character parameters, such as joint rotations and end effector positions. Thus, various 3D user interfaces have been proposed to make animating easier, but they typically provide less accuracy. The proposed interface combines a mouse with the Leap Motion device to provide 3D input. A usability study showed that users preferred the Leap Motion over a mouse as a 3D gestural input device. The Leap Motion drastically decreased the number of required operations and the task completion time, especially for novice users.

Storyboarding is widely used for creating 3D animations. Animators use the 2D sketches in storyboards as references to craft the desired 3D animations through a trial-and-error process. The traditional approach requires exceptional expertise and is both labor-intensive and time-consuming. Consequently, there is a high demand for automated methods that can directly translate 2D storyboard sketches into 3D animations. This task is under-explored to date and inspired by the significant advancements of motion diffusion models, we propose to address it from the perspective of conditional motion synthesis. We thus present Sketch2Anim , composed of two key modules for sketch constraint understanding and motion generation. Specifically, due to the large domain gap between the 2D sketch and 3D motion, instead of directly conditioning on 2D inputs, we design a 3D conditional motion generator that simultaneously leverages 3D keyposes, joint trajectories, and action words, to achieve precise and fine-grained motion control. Then, we invent a neural mapper dedicated to aligning user-provided 2D sketches with their corresponding 3D keyposes and trajectories in a shared embedding space, enabling, for the first time , direct 2D control of motion generation. Our approach successfully transfers storyboards into high-quality 3D motions and inherently supports direct 3D animation editing, thanks to the flexibility of our multi-conditional motion generator. Comprehensive experiments and evaluations, and a user perceptual study demonstrate the effectiveness of our approach. The code, data, trained models, and sketch-based motion designing interface are at https://zhongleilz.github.io/Sketch2Anim/.

This paper mainly analyzes the application advantages of virtual reality technology in medical experiment teaching, and integrates the construction and structure of medical experiment teaching system. The application of virtual reality technology in medical experiment teaching can timely solve the problem of low simulation degree in clinical experiment teaching, and design a knowledge teaching system according to practical knowledge and theoretical knowledge. Experimental interactive model and 3d model system of virtual reality technology can improve the interaction of medical experiment learning and make students master the operation process of medical knowledge more easily. At the same time, using the virtual visualization function of virtual reality technology, medical experiment teaching can quickly simulate various organs of the human body and the diseased parts of patients, so as to provide students with fast surgery, which greatly realizes the development of intelligent operation of medical experiment teaching and builds a comprehensive medical experiment teaching model. Virtual reality technology provides strong technical support for the development of medical experiment teaching.

Computer graphics hardware supporting real-time interactive 3D animation has the potential to support effective user interfaces by enabling virtual 3D workspaces. However, this potential requires development of viewpoint movement techniques that support rapid and controlled movement through workspaces. Rapid movement through large distances avoids wasted work time; controlled movement near target objects allows the user to examine and interact with objects in the workspace. Current techniques for viewpoint movement typically use high velocities to cover distances rapidly, but high velocities are hard to control near objects. This paper describes a new technique for targeted viewpoint movement that solves this problem. The key idea is to have the user indicate a point of interest (target) on a 3D object and use the distance to this target to move the viewpoint logarithmically, by moving the same relative percentage of distance to the target on every animation cycle. the result is rapid motion over distances that slows as the viewpoint approaches the target object. The technique can be used with 2D and multidimensional input devices. We also extend the technique to move objects in the workspace.

In desktop applications 3D input devices are mostly operated by the non-dominant hand to control 3D viewpoint navigation, while selection and geometry manipulations are handled by the dominant hand using the regular 2D mouse. This asymmetric bi-manual interface is an alternative to commonly used keyboard and mouse input, where the non-dominant hand assists the dominant hand with keystroke input to toggle modes. Our first study compared the keyboard and mouse interface to bi-manual interfaces using the 3D input devices SpaceTraveller and Globefish in a coarse spatial orientation task requiring egocentric and exocentric viewpoint navigation. The different interface configurations performed similarly with respect to task completion times, but the bi-manual techniques resulted in significantly less errors. This result is likely to be due to better workload balancing between the two hands allowing the user to focus on a single task for each hand. Our second study focused on a bi-manual 3D point selection task, which required the selection of small targets and good depth perception. The Globefish interface employing position control for rotations performed significantly better than the SpaceTraveller interface for this task.

We present a new interactive modeling technique based on the notion of sculpting a solid material. A sculpting tool is controlled by a 3D input device and the material is represented by voxel data; the tool acts by modifying the values in the voxel array, much as a paint program's paintbrush modifies bitmap values. The voxel data is converted to a polygonal surface using a marching-cubes algorithm; since the modifications to the voxel data are local, we accelerate this computation by an incremental algorithm and accelerate the display by using a special data structure for determining which polygons must be redrawn in a particular screen region. We provide a variety of tools: one that cuts away material, one that adds material, a sandpaper tool, a heat gun, etc. The technique provides an intuitive direct interaction, as if the user were working with clay or wax. The models created are free-form and may have complex topology; however, they are not precise, so the technique is appropriate for modeling a boulder or a tooth but not for modeling a crankshaft.

Tablets and smartphones are now increasingly common and the interfaces are predominantly touch-based and often multi-touch. More and more schools are testing them with their students in the hope of bringing educational benefits. With this new type of device, new interactions become possible. Many studies have been done on the manipulation of 3D objects with 2D input devices, but we are only at the beginning of studies that link the needs of pedagogy with the possibilities of these new types of interactions. FINGERS© ((Find INteractions for Geometry learNERS) )is an application for learning spatial geometry. It is intended for pupils aged 9 to 12. The interactions have been designed with the teachers. Some interactions are specific to 3D geometry (translations 3 DOF, rotations, nets, combinations of cubes, etc) and others are general like naming or multi-selection. Many gesture grammars offer a set of interactions to select an object or a group of objects. Multi-taps or lassoing around an area are commonly adopted interactions. The execution of geometry exercises requires the construction of other interactions such as multi-criteria selection. The real question is how to introduce a parameter into the tactile selection. This seemingly simple interaction is in fact complex to implement. One of the problems is often the large amount of information displayed on the screen. The amount of information displayed does not allow the use of conventional selection modes. The difficulties encountered are, for example, that objects can hide, their size is too small, their number is too large. This selection could be based on physical similarity (cube, cylinder, pyramid, sphere, etc.), similarity of colour, orientation in space, etc. For example, how to select all red cubes, how to select all green objects, how to select all cylinders. The real question is how to define a grammar of tactile gestures that allows parameter selection. After presenting the limitations of current solutions, we present the solutions developed in FINGERS©. We explain how they allow a simple and intuitive "multi-criteria" selection by using physical or colorometric characteristics of objects. We present examples from the implementation of this selection mode in the FINGERS software.