Camera Path Research Articles

Cybersickness Reduction via Gaze-Contingent Image Deformation

Virtual reality has ushered in a revolutionary era of immersive content perception. However, a persistent challenge in dynamic environments is the occurrence of cybersickness arising from a conflict between visual and vestibular cues. Prior techniques have demonstrated that limiting illusory self-motion, so-called vection, by blurring the peripheral part of images, introducing tunnel vision, or altering the camera path can effectively reduce the problem. Unfortunately, these methods often alter the user's experience with visible changes to the content. In this paper, we propose a new technique for reducing vection and combating cybersickness by subtly lowering the screen-space speed of objects in the user's peripheral vision. The method is motivated by our hypothesis that small modifications to the objects' velocity in the periphery and geometrical distortions in the peripheral vision can remain unnoticeable yet lead to reduced vection. This paper describes the experiments supporting this hypothesis and derives its limits. Furthermore, we present a method that exploits these findings by introducing subtle, screen-space geometrical distortions to animation frames to counteract the motion contributing to vection. We implement the method as a realtime post-processing step that can be integrated into existing rendering frameworks. The final validation of the technique and comparison to an alternative approach confirms its effectiveness in reducing cybersickness.

Movement-based cognitive training does not significantly shorten the learning curve for acquiring arthroscopic basic skills.

Skilful arthroscopy requires an aboveaverage level of manual dexterity. It is evident that particular motor skills can be learned and trained before arthroscopic training. The aim of this prospective cohort study was to investigate the impact of movement-related cognitive training on the learning curve during arthroscopic basic training. Fifty right-handed participants without arthroscopic experience were matched to an intervention group (n = 25) and a control group (n = 25). Prior to basic arthroscopic skill training with a simulator, the intervention group underwent 12 weeks of movement-related cognitive training. Cognitive and motor skills were assessed in both groups by using standardised tests (CogniFit test, angle reproduction test, two-arm coordination test) as a pretest and, for the intervention group, again before arthroscopic training as a posttest. For arthroscopic simulator training, three tasks ('Telescoping', 'Periscoping', 'Triangulation') from the Fundamentals of Arthroscopic Surgery Training module were selected and practiced 10 times with the camera in the right and left hands. The learning progress was quantified by exercise time, camera path length and hook path length. No significant differences in sex distribution, age distributionor the results of the pretests between the intervention group (n = 21) and the control group (n = 25) were found (n.s.). The intervention group improved significantly from the pretest to the posttest in the CogniFit (p = 0.003) and two-arm coordination test in terms of time (p < 0.001) and errors (p = 0.002) but not in the angle reproduction test. No significant differences were found between the groups for the three arthroscopic tasks. The hypothesis that movement-related cognitive training shortens the learning curve for acquiring arthroscopic basic skills cannot be confirmed. Other factors influencing the learning curvesuch as talent, teaching method and motivation have a greater impact on the acquisition of complex motor skills. Level II.

OptFlowCam: A 3D‐Image‐Flow‐Based Metric in Camera Space for Camera Paths in Scenes with Extreme Scale Variations

AbstractInterpolation between camera positions is a standard problem in computer graphics and can be considered the foundation of camera path planning. As the basis for a new interpolation method, we introduce a new Riemannian metric in camera space, which measures the 3D image flow under a small movement of the camera. Building on this, we define a linear interpolation between two cameras as shortest geodesic in camera space, for which we provide a closed‐form solution after a mild simplification of the metric. Furthermore, we propose a geodesic Catmull‐Rom interpolant for keyframe camera animation. We compare our approach with several standard camera interpolation methods and obtain consistently better camera paths especially for cameras with extremely varying scales.

DMCVS: Decomposed motion compensation‐based video stabilization

AbstractWith the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.

Camera Path Generation for Triangular Mesh Using Toroidal Patches

Triangular mesh data structures are principal in computer graphics, serving as the foundation for many 3D models. To effectively utilize these 3D models across diverse industries, it is important to understand the model’s overall shape and geometric features thoroughly. In this work, we introduce a novel method for generating camera paths that emphasize the model’s local geometric characteristics. This method uses a toroidal patch-based spatial data structure, approximating the mesh’s faces within a predetermined tolerance ϵ, encapsulating their geometric intricacies. This facilitates the determination of the camera position and gaze path, ensuring the mesh’s key characteristics are captured. During the path construction, we create a bounding cylinder for the mesh, project the mesh’s faces and associated toroidal patches onto the cylinder’s lateral surface, and sequentially select grids of the cylinder containing the highest number of toroidal patches as we traverse the lateral surface. The centers of the selected grids are used as control points for a periodic B-spline curve, which serves as our foundational path. After initial curve generation, we generated camera position and gaze path from the curve by multiplying factors to ensure a uniform camera amplitude. We applied our method to ten triangular mesh models, demonstrating its effectiveness and adaptability across various mesh configurations.

Minimum Latency Deep Online Video Stabilization and Its Extensions.

We present a novel deep camera path optimization framework for minimum latency online video stabilization. Typically, a stabilization pipeline consists of three steps: motion estimation, path smoothing, and novel view synthesis. Most previous methods concentrate on motion estimation while path optimization receives less attention, particularly in the crucial online setting where future frames are inaccessible. In this work, we adopt off-the-shelf high-quality deep motion models for motion estimation and focus only on the path optimization. Specifically, our camera path smoothing network takes a short 2D camera path in a sliding window as input and outputs the stabilizing warp field of the last frame, which warps the coming frame to its stabilized position. We explore three motion densities: a global single camera path, local mesh-based bundled paths, and dense flow paths. A hybrid loss and an efficient motion smoothing attention (EMSA) module are proposed for spatially and temporally consistent path smoothing. Moreover, we build a motion dataset that contains stable and unstable motion pairs for training. Extensive experiments demonstrate that our method surpasses state-of-the-art online stabilization methods and rivals the performance of offline methods, offering compelling advancements in the field of video stabilization. Code and dataset are available at https://github.com/liuzhen03/NNDVS.

Hand Dominance Is Not of Significance in Performing Fundamental Arthroscopic Skills Simulation Training Tasks

To compare the performance of the dominant and nondominant hand during fundamental arthroscopic simulator training. Surgical trainees who participated in a 2-day simulator training course between 2021 and 2023 were classified, according to their arthroscopic experience in beginners and competents. Only right-handed individuals with complete data sets were included in the study. Ambidexterity was trained using a box trainer (Fundamentals of Arthroscopic Surgery Training, Virtamed AG, Schlieren, Switzerland).Two tasks, periscoping for learning camera guidance and triangulation for additional instrument handling, were performed 4 times with the camera in the dominant hand and then in the nondominant hand. For each task, exercise time, camera path length, and instrument path length were recorded and analyzed. Out of 94 participants 74 right-handed individuals (22 females, 52 males) were classified to novices (n= 43, less than 10 independently performed arthroscopies) and competents (n= 31, more than 10 independently performed arthroscopies). Competents performed significantly better than novices. No significant difference was found after changing the guiding hand for the camera from the dominant to the nondominant hand regarding the camera path length and the instrument path length. Notably, tasks were performed even faster when using the camera in the nondominant hand. Our data demonstrate that the learned manual skills during basic arthroscopic training are quickly transferred to the contralateral side. In consequence, additional fundamental skills training for camera guidance and instrument handling of the nondominant hand are not necessary. For skillful arthroscopy, camera guidance and instrument handing must be equally mastered with both hands. It is important to understand how hand dominance may affect learning during arthroscopic simulator training.

A robust video stabilization method for camera shoot in mobile devices using GMM-based motion estimator

The application of video stabilization methods to mobile devices with low computing power is an important issue. In this study, we aim to provide a robust video stabilization solution for such mobile devices with limited computing resources. We first propose a fast feature extraction method based on a block-wise gradient. Then, we extract motion information through feature point pairs and rebuild the camera path. Finally, the data were smoothed and the corresponding compensation matrix was calculated to obtain a stable video. However, large foreground objects may lead to incorrect motion estimation. Thus, we propose a fast foreground segmentation method based on GMM for jitter scenes to eliminate foreground features and obtain a more accurate camera trajectory. The experimental results show that the proposed method is robust against large foreground objects and exhibits good real-time performance.

PRACTICAL IMPLEMENTATION OF PHOTOGRAMMETRY FOR THE MODELLING OF A CYLINDRICAL HISTORICAL BUILDING

Abstract. This work deals with the image capture for close-range photogrammetry in the context of the modelling of a round tower with repetitive texture (made of bricks), both inside and outside. For that purpose we test different acquisition strategies that differ in terms of camera path (straight or circular) and number of images acquired at each camera position, that is with or without oblique images. Besides, the formula to compute the curvilinear base according to a given overlap rate between successive frontal photos are written for each strategy. The comparisons rely on the observation of the aspect of the different dense point clouds (noise, holes), the deformation seen in the orthomosaics and values extracted from the photogrammetric projects such as metric accuracies with GCP. Our results confirm some art rules.

Nanotilus: Generator of Immersive Guided-Tours in Crowded 3D Environments.

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This article proposes Nanotilus-a new visibility and guidance approach for very dense environments that generates an endoscopic inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multi-scale, multi-shell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models - SARS-CoV-2 and HIV. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.

Deep Online Video Stabilization Using IMU Sensors

In this paper, we propose a deep learning based sensor-driven method for online video stabilization. This method utilizes euler angle and acceleration estimated from the gyroscope and accelerator to assist stable video reconstruction. We introduce two simple sub-networks for trajectory optimization, the first network exploits real unstable trajectory and acceleration of camera to detect shooting scenarios. This network also generates an attention mask to choose scenario-specific features adaptively. Then the second network predicts smooth camera paths based on real unstable trajectory using LSTM under the supervision of the former mask. The output of trajectory optimization network is filtered with a two-step modification process to guarantee the smoothness. The real and smoothed camera paths are then utilized as guidance to projectively generate stable frames. We also capture videos with sensor data covering seven typical shooting scenarios, and design a groundtruth generation method to construct pseud-labels. Moreover, the trajectory smoothing network allows 3 or 10 frames buffer as the future information to construct a lookahead filter. Experimental results show that our online method could outperform other offline state-of-the-art methods in several shaky video clips with fewer buffer frames over both general and low-quality videos. Furthermore, our method could effectively reduce running times without image content analysis, and the stabilization efficiency reaches 25 fps on 1080p videos.

Real-Time Digital Video Stabilization of Bioinspired Robotic Fish Using Estimation-and-Prediction Framework

The rhythmic movement of bioinspired robotic fish brings about undesirable visual jitter. Note that the unstable camera path of this kind of robot is characterized by obvious regularity and predictability, of which traditional stabilization methods have not made full advantage. This article proposes a novel estimation-and-prediction framework for real-time digital video stabilization of bioinspired robotic fish. First, based on the attitude information of an inertial measurement unit (IMU), a camera–IMU model is established, where the homography transformation with eight degrees of freedom (DOFs) is reduced to translation transformation with two DOFs. Second, traditional optical flow and gray projection methods as well as a novel translation estimation network are employed to estimate the translations between consecutive frames. Third, a lightweight long short-term memory (LSTM) network is constructed, allowing remarkable prediction and smoothing of the camera path. Finally, aquatic experiments under various scenarios are conducted on a manta-inspired robot, demonstrating the effectiveness of the proposed method. Specifically, compared with the state-of-the-art commercial offline stabilization software, our online stabilization algorithm achieves approximate visual stability and remarkably faster stabilization speed. The obtained results shed light on visual sensing and control applications of bioinspired underwater vehicles.

View Invariants for Three-Dimensional Points with Constrained Observer Motion

Images from cameras are a common source of navigation information for a variety of vehicles. Such navigation often requires the matching of observed objects (e.g., landmarks, beacons, stars) in an image to a catalog (or map) of known objects. In many cases, this matching problem is made easier through the use of invariants. However, if the objects are modeled as three-dimensional points in general position, it has long been known that there are no invariants for a camera that is also in general position. This work discusses how invariants are introduced when the camera’s motion is constrained to a line, and proves that this is the only camera path along which invariants are possible. Algorithms are presented for computing both the invariants and the location for a camera undergoing rectilinear motion. The applicability of these ideas is discussed within the context of trains, aircraft, and spacecraft.

A Drone Video Clip Dataset and its Applications in Automated Cinematography

AbstractDrones became popular video capturing tools. Drone videos in the wild are first captured and then edited by humans to contain aesthetically pleasing camera motions and scenes. Therefore, edited drone videos have extremely useful information for cinematography and for applications such as camera path planning to capture aesthetically pleasing shots. To design intelligent camera path planners, learning drone camera motions from these edited videos is essential. However, first, this requires to filter drone clips and extract their camera motions out of these edited videos that commonly contain both drone and non‐drone content. Moreover, existing video search engines return the whole edited video as a semantic search result and cannot return only drone clips inside an edited video. To address this problem, we proposed the first approach that can automatically retrieve drone clips from an unlabeled video collection using high‐level search queries, such as “drone clips captured outdoor in daytime from rural places”. The retrieved clips also contain camera motions, camera view, and 3D reconstruction of a scene that can help develop intelligent camera path planners. To train our approach, we needed numerous examples of edited drone videos. To this end, we introduced the first large‐scale dataset composed of edited drone videos. This dataset is also used for training and validating our drone video filtering algorithm. Both quantitative and qualitative evaluations have confirmed the validity of our method.

Meditation Using a Mobile App Improves Surgery Trainee Performance: A Simulation-Based Randomized Controlled Trial

To primarily investigate: (1) whether a 10-minute instant meditation practice using a mobile app could enhance arthroscopy performance and (2) whether a 10-day app-based meditation could reduce short-term arthroscopic skills deterioration. Orthopaedic residents with no previous experience in arthroscopy and meditation were randomly assigned to groups A, B, and C. After initial standard competency-based arthroscopy training on the simulator on day 1, a pretest was performed via the simulator by all participants to assess their initial level of performance, then groups A and B were required to practice app-based mindfulness meditation 10 min/day for 10 consecutive days while group C did nothing. On day 11, all participants returned to perform a posttest. Before the posttest, the participants in group A practiced app-based meditation (10 minutes), whereas groups B and C had no intervention. In total, 43 participants were included and reached similar level of performance after initial training phase in day 1. On day 11, participants in group A had statistically a better instant arthroscopy performance than group B, with greater total score (mean difference [MD] 3.57; P < .001), less completion time (MD -42.89 seconds; P= .001), shorter camera (MD -23.38 cm; P < .001) and grasper (MD -15.23 cm; P= .002) path length, and less cartilage injury (MD -1.07%; P= .012). Participants in group B had less skills deterioration than group C, with better total score (MD -5.42; P < .001), less completion time (MD 51.96s; P= .002), camera path length (MD 28.41 cm; P= .007), and cartilage injury (MD 1.19%; P= .038). Meditation training using a mobile app enhanced instant simulation-based arthroscopy performance and reduced short-term skills deterioration of orthopaedic residents with no arthroscopy hands-on experience. A meditation using mobile app for clinicians and educators should be incorporated into simulation-based arthroscopy curriculums and perhaps clinical settings to improve arthroscopy performance and mental health of orthopaedic residents without any previous arthroscopy experience.

Effective Skill Transfer From Fundamentals of Arthroscopic Surgery Training to Shoulder Arthroscopic Simulator in Novices.

To investigate whether novices could improve performance on a shoulder arthroscopic simulator (high-fidelity) through short-term training on a Fundamentals of Arthroscopic Surgery Training (FAST) simulator (low-fidelity). Twenty-eight novices with no experience in arthroscopy were recruited to perform a pre-test on a shoulder arthroscopic simulator. Then they were randomized into two groups: the experimental group practiced five modules on the FAST simulator three times, and the control group did nothing. The experimental group performed a post-test immediately after FAST simulator practice. Control group rested for 70minutes after experiencing pre-test before performing post-test. All parameters were recorded by the simulator. The experimental group outperformed the control group in terms of total score, procedure time, camera path length, and grasper path length. However, there was no statistical difference in scratching of humerus cartilage or glenoid cartilage. Significant differences were found in the improvement of both groups in total score, procedure time, and camera path length. Arthroscopic skills gained after short-term training on FAST simulator could be transferred to the shoulder arthroscopic simulator. This research provides important evidence of the benefits of FAST simulator in shoulder arthroscopy training program.

Volumetric Multi‐View Rendering

AbstractRendering photo‐realistic images using Monte Carlo path tracing often requires sampling a large number of paths to reach acceptable levels of noise. This is particularly the case when rendering participating media, that complexify light paths with multiple scattering events. Our goal is to accelerate the rendering of heterogeneous participating media by exploiting redundancy across views, for instance when rendering animated camera paths, motion blur in consecutive frames or multi‐view images such as lenticular or light‐field images. This poses a challenge as existing methods for sharing light paths across views cannot handle heterogeneous participating media and classical estimators are not optimal in this context. We address these issues by proposing three key ideas. First, we propose new volume shift mappings to transform light paths from one view to another within the recently introduced null‐scattering framework, taking into account changes in density along the transformed path. Second, we generate a shared path suffix that best contributes to a subset of views, thus effectively reducing variance. Third, we introduce the multiple weighted importance sampling estimator that benefits from multiple importance sampling for combining sampling strategies, and from weighted importance sampling for reducing the variance due to non contributing strategies. We observed significant reuse when views largely overlap, with no visible bias and reduced variance compared to regular path tracing at equal time. Our method further readily integrates into existing volumetric path tracing pipelines.

Implementation of NAO Robot Maze Navigation Based on Computer Vision and Collaborative Learning.

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub, airports, or for evacuation of disaster zones. Many methodologies have been explored to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have become more popular in this kind of scenarios, given their robustness and scalability. In this paper, we implement an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be capable of solving the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we showcase some initial experimentation in a pair of robots with different mechanical characteristics. Further implementations of this work include communicating the robots for other tasks, such as teaching assistance, remote classes, and other innovations in higher education.

Hand dominance and experience improve bimanual performance on arthroscopic simulator task

PurposeThe aim of this study was to identify if experience in arthroscopy confers ambidexterity to the operator and the role of baseline characteristics in arthroscopic simulator performance.MethodsA prospective comparative study was carried out across four regional Orthopaedic training centres. Participants were divided into novice, intermediate or experienced groups based on arthroscopic experience. Baseline demographics including age, sex, handedness, and gaming history were also collected. Following familiarisation with the procedure, participants were asked to complete a simulated task requiring bimanual control consisting of visualisation with camera control and manipulation of highlighted objects using a grasping instrument. One attempt using camera control and grasping accuracy per hand was performed by each participant, with scores for each hand collected for analysis. Performance scores for camera alignment, camera path length, grasper path length and grasping efficiency were collected. Time taken to completion was also noted for each attempt.ResultsFifty-six participants were recruited to the study. A significant difference in grasping efficiency between groups in the dominant hand was demonstrated (p = 0.013). Novices demonstrated laterality with superior performance in grasping efficiency in the dominant hand (p = 0.001). No significant difference was noted between dominant and non-dominant hand performance in the experienced group.ConclusionArthroscopic simulation-based training is a valuable learning tool for orthopaedic training. This study demonstrated that experienced orthopaedic surgeons have a greater degree of ambidexterity than intermediate or novice groups, hypothesised by authors to be conferred through conventional orthopaedic training. Dedicated bimanual control tasks to reduce laterality in trainees should be incorporated in simulated surgical curricula.Level of evidenceIII.

Is the Virtual Reality Fundamentals of Arthroscopic Surgery Training Program a Valid Platform for Resident Arthroscopy Training?

Training in arthroscopy is associated with a steep learning curve for trainees and bears risks for patients. Virtual reality (VR) arthroscopy simulation platforms seek to overcome this and to provide a safe environment for surgical learners. The Fundamentals of Arthroscopic Surgery Training (FAST) program is one such platform. It is currently not known whether the VR FAST program can be employed as a useful teaching or examination tool to assess the basic arthroscopic skills of surgical trainees. (1) Does the VR FAST program differentiate among novice, intermediate, and expert arthroscopists? (2) Does ambidextrous performance in the VR FAST program correlate with arthroscopic experience? We prospectively recruited orthopaedic interns (novices), residents (intermediates), and fellows and attendings (experts) to complete the VR FAST program over a 1-year period from four major orthopaedic training programs on a voluntary basis. Sixty-six of 156 invited orthopaedic surgeons participated: 26 of 50 novices (16 men and 10 women), 27 of 65 intermediates (20 men and seven women), and 13 of 41 experts (10 men and three women). Surgeons of any arthroscopic experience were included, with only those with prior experience on the VR FAST program being excluded. The program consists of eight modules: three basic camera modules (Image Centering, Horizon Control, and Telescoping), three advanced camera modules (Periscoping, Trace the Line, and Trace the Curve), and two instrumented bimanual-dexterity modules (Probe Triangulation and Gather the Stars). Time taken to complete each task and measures of economy of movement (camera and instrument path length, camera alignment) were used as measures of arthroscopic experience. Every participant completed the modules using their dominant and nondominant hands. Equality in proficiency in completing the tasks using the dominant and nondominant hands were determined to be measures of arthroscopic experience. Due to the large number of outcome variables, only p values < 0.01 were considered to be statistically significant. Six of eight VR FAST modules did not discriminate among novice, intermediate, and expert arthroscopy participants. However, two did, and the ones that were most effective at distinguishing participants by level of experience were the Periscoping and Gather the Stars modules. For the Periscoping module using the dominant hand, novices required longer to complete the task with a median time of 231 seconds (IQR 149 to 358) and longer camera path length median of 191 cm (IQR 128 to 273) compared with intermediates who needed 127 seconds (IQR 106 to 233) and 125 cm (IQR 92 to 159) and experts who needed 121 seconds (IQR 93 to 157) and 119 cm (IQR 90 to 134) (p = 0.001 and p = 0.003, respectively). When using the nondominant hand, novices took longer to complete the task with a median time of 231 seconds (IQR 170 to 350) and longer camera path length 204 cm (IQR 169 to 273) compared with intermediates who required 132 seconds (IQR 97 to 162) and 111 cm (IQR 88 to 143) and experts who needed 119 seconds (IQR 104 to 183) and 120 cm (IQR 108 to 166) (p < 0.001 and p < 0.001, respectively). For the Gather the Stars module using the nondominant hand, only the novices needed longer to complete the task at a median of 131 seconds (IQR 112 to 157) and needed a longer grasper path length of 290 cm (IQR 254 to 332) compared with intermediates who needed 84 seconds (IQR 72 to 119) and 232 cm (IQR 195 to 254) and experts who needed 98 seconds (IQR 87 to 107) and 244 cm (IQR 215 to 287) (p < 0.001 and p = 0.001, respectively). Six of eight VR FAST modules did not demonstrate construct validity, and we found no correlation between arthroscopic experience and ambidextrous performance. Two modules demonstrated construct validity; however, refinement and expansion of the modules is needed with further validation in large prospective trials so that pass-fail thresholds can be set for use in high-stakes examinations. Most VR FAST modules were not discriminatory; however, they can form essential conceptual and procedural building blocks in an arthroscopic curriculum that are beneficial for novices when developing key psychomotor skills. In their present format, however, they are unsuitable for assessing arthroscopic proficiency.