Intermediate Frames Research Articles

Automatic Inbetweening for Stroke‐Based Painterly Animation

AbstractPainterly 2D animation, like the paint‐on‐glass technique, is a tedious task performed by skilled artists, primarily using traditional manual methods. Although CG tools can simplify the creation process, previous works often focus on temporal coherence, which typically results in the loss of the handmade look and feel. In contrast to cartoon animation, where regions are typically filled with smooth gradients, stroke‐based stylized 2D animation requires careful consideration of how shapes are filled, as each stroke may be perceived individually. We propose a method to generate intermediate frames using example keyframes and a motion description. This method allows artists to create only one image for every five to 10 output images in the animation, while the automatically generated intermediate frames provide plausible inbetween frames.

Higgs inflation via a metastable standard model potential, generalised renormalisation frame prescriptions and predictions for primordial gravitational waves

Higgs Inflation via a metastable Standard Model Higgs Potential is possible if the effective Planck mass in the Jordan frame increases after inflation ends. Here we consider the predictions of this model independently of the dynamics responsible for the Planck mass transition. The classical predictions are the same as for conventional Higgs Inflation. The quantum corrections are dependent upon the conformal frame in which the effective potential is calculated. We generalise beyond the usual Prescription I and II renormalisation frame choices to include intermediate frames characterised by a parameter α. We find that the model predicts a well-defined correlation between the values of the scalar spectral index ns and tensor-to-scalar ratio r. For values of ns varying between the 2-σ Planck observational limits, we find that r varies between 0.002 and 0.005 as ns increases, compared to the classical prediction of 0.003. Therefore significantly larger or smaller values of r are possible, which are correlated with larger or smaller values of ns . In addition, the model can be compatible with the larger values of ns predicted by Early Dark Energy solutions to the Hubble tension, with correspondingly larger values of r. The model can be tested via the detection of primordial gravitational waves by the next generation of CMB polarisation experiments.

Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon

Abstract. Databases of 3D paleoclimate model simulations are increasingly used within global biogeochemical models for the Phanerozoic Eon. This improves the accuracy of the surface processes within the biogeochemical models, but the approach is limited by the availability of large numbers of paleoclimate simulations at different pCO2 levels and for different continental configurations. In this paper we apply the Frame Interpolation for Large Motion (FILM) deep learning method to a set of Phanerozoic paleoclimate model simulations to upscale their time resolution from one model run every ∼25 million years to one model run every 1 million years (Myr). Testing the method on a 5 Myr time-resolution set of continental configurations and paleoclimates confirms the accuracy of our approach when reconstructing intermediate frames from configurations separated by up to 40 Myr. We then apply the method to upscale the paleoclimate data structure in the SCION climate-biogeochemical model. The interpolated surface temperature and runoff are reasonable and present a logical progression between the original key frames. When updated to use the high-time-resolution climate data structure, the SCION model predicts climate shifts that were not present in the original model outputs due to its previous use of widely spaced datasets and simple linear interpolation. We conclude that a time resolution of ∼10 Myr in Phanerozoic paleoclimate simulations is likely sufficient for investigating the long-term carbon cycle and that deep learning methods may be critical in attaining this time resolution at reasonable computational expense, as well as for developing new fully continuous methods in which 3D continental processes are able to translate over a moving continental surface in deep time. However, the efficacy of deep learning methods in interpolating runoff data, compared to that of paleogeography and temperature, is diminished by the heterogeneous distribution of runoff. Consequently, interpolated climates must be confirmed by running a paleoclimate model if scientific conclusions are to be based directly on them.

The Optimization of Numerical Algorithm Parameters with a Genetic Algorithm to Animate Letters of the Sign Alphabet

At present, the development of animation-based works for human–computer interaction applications has increased. To generate animations, actions are pre-recorded and animation flows are configured. In this research, from two images of letters of the sign language alphabet, intermediate frames were generated using a numerical traced algorithm based on homotopy. The parameters of a homotopy curve were optimized with a genetic algorithm to generate intermediate frames. In the experiments performed, sequences where a person executes pairs of letters in sign language were recorded and animations of the same pairs of letters were generated with the proposed method. Subsequently, the similarity of the real sequences to the animations was measured using Dynamic Time Wrapping. The results obtained show that the images obtained are consistent with their execution by a person. Animation files between sign pairs were created from sign images, with each file weighing an average of 18.3 KB. By having sequences between pairs of letters it is possible to animate words and sentences. The animations generated by this homotopy-based animation method optimized with a genetic algorithm can be used in various deaf interaction applications to provide assistance. From several pairs of letters a file base was generated using the animations between pairs of letters; with these files you can create animations of words and sentences.

EMCFN: Edge-based Multi-scale Cross Fusion Network for video frame interpolation

Video frame interpolation (VFI) is used to synthesize one or more intermediate frames between two frames in a video sequence to improve the temporal resolution of the video. However, many methods still face challenges when dealing with complex scenes involving high-speed motion, occlusions, and other factors. To address these challenges, we propose an Edge-based Multi-scale Cross Fusion Network (EMCFN) for VFI. We integrate a feature enhancement module (FEM) based on edge information into the U-Net architecture, resulting in richer and more complete feature maps, while also enhancing the preservation of image structure and details. This contributes to generating more accurate and realistic interpolated frames. At the same time, we use a multi-scale cross fusion frame synthesis model (MCFM) composed of three GridNet branches to generate high-quality interpolation frames. We have conducted a series of experiments and the results show that our model exhibits satisfactory performance on different datasets compared with the state-of-the-art methods.

Design-Optimization of Conventional Steel Structures for Realization of the Sustainable Development Objectives Using Metaheuristic Algorithm

The construction industry presents a significant environmental challenge due to its substantial environmental footprint, utilization of limited natural resources, and contribution to pollution and climate change. Additionally, optimizing the weight, cost, and duration of construction is crucial for enhancing serviceability, flexibility, efficiency, and profitability. In this research, the relationship between structure weight and other objective functions was explored using the single-objective gray wolf algorithm to investigate their impact on carbon footprint, water footprint, and construction time. Furthermore, employing a multi-objective optimization algorithm, a building structure was optimized for three systems featuring different structural frames based on the specified objective functions. The results revealed that the structure with intermediate steel moment-resisting frames exhibited the shortest construction time but incurred the highest construction cost. Conversely, the structure with intermediate steel moment-resisting frames with special steel concentric bracing demonstrated the lowest carbon footprint and water footprint among the studied structural frames. Consequently, the structure with intermediate steel moment-resisting frames with special concentric steel bracing was proposed as a green structure, emphasizing its environmentally friendly characteristics.

Moving Object Detection in Freely Moving Camera via Global Motion Compensation and Local Spatial Information Fusion.

Motion object detection (MOD) with freely moving cameras is a challenging task in computer vision. To extract moving objects, most studies have focused on the difference in motion features between foreground and background, which works well for dynamic scenes with relatively regular movements and variations. However, abrupt illumination changes and occlusions often occur in real-world scenes, and the camera may also pan, tilt, rotate, and jitter, etc., resulting in local irregular variations and global discontinuities in motion features. Such complex and changing scenes bring great difficulty in detecting moving objects. To solve this problem, this paper proposes a new MOD method that effectively leverages local and global visual information for foreground/background segmentation. Specifically, on the global side, to support a wider range of camera motion, the relative inter-frame transformations are optimized to absolute transformations referenced to intermediate frames in a global form after enriching the inter-frame matching pairs. The global transformation is fine-tuned using the spatial transformer network (STN). On the local side, to address the problem of dynamic background scenes, foreground object detection is optimized by utilizing the pixel differences between the current frame and the local background model, as well as the consistency of local spatial variations. Then, the spatial information is combined using optical flow segmentation methods, enhancing the precision of the object information. The experimental results show that our method achieves a detection accuracy improvement of over 1.5% compared with the state-of-the-art methods on the datasets of CDNET2014, FBMS-59, and CBD. It demonstrates significant effectiveness in challenging scenarios such as shadows, abrupt changes in illumination, camera jitter, occlusion, and moving backgrounds.

Video frame interpolation via spatial multi‐scale modelling

AbstractVideo frame interpolation (VFI) is a technique that synthesises intermediate frames between adjacent original video frames to enhance the temporal super‐resolution of the video. However, existing methods usually rely on heavy model architectures with a large number of parameters. The authors introduce an efficient VFI network based on multiple lightweight convolutional units and a Local three‐scale encoding (LTSE) structure. In particular, the authors introduce a LTSE structure with two‐level attention cascades. This design is tailored to enhance the efficient capture of details and contextual information across diverse scales in images. Secondly, the authors introduce recurrent convolutional layers (RCL) and residual operations, designing the recurrent residual convolutional unit to optimise the LTSE structure. Additionally, a lightweight convolutional unit named separable recurrent residual convolutional unit is introduced to reduce the model parameters. Finally, the authors obtain the three‐scale decoding features from the decoder and warp them for a set of three‐scale pre‐warped maps. The authors fuse them into the synthesis network to generate high‐quality interpolated frames. The experimental results indicate that the proposed approach achieves superior performance with fewer model parameters.

Probabilistic alternate path analysis of steel moment-resisting frames

This research presents a probabilistic assessment of progressive collapse in intermediate Steel Moment-Resisting Frames (SMRFs) subjected to different levels of column damage. Damage levels were represented by gradual reductions in column stiffness to the extent that tensile forces are created in columns sustaining large deformations, and columns were allowed to enter completely plastic zone. To this aim, low- to mid-rise structures with 4, 8, and 12 stories were examined. The effect of composite slabs on the vertical displacement response of SMRFs was taken as a variable. A number of 11 column damage scenarios were defined with either one or two damaged columns, located at different positions in the plan, which were applied to all floor levels. Incremental dynamic analysis was conducted for each damage scenario using the finite element OpenSees framework. Moreover, a state-of-the-art approach was employed in fragility analysis. The results showed that higher floors are more sensitive to partial damage due to less structural components involved in progressive collapse. However, in case of large damages, lower floors prove to be more critical due to greater deal of gravity loads. Shorter and taller structures perform better in large and partial damages to column, respectively. Moreover, the failure probability of SMRFs reduced by considering the composite slab stiffness. Subjected to single-column damage scenarios, SMRFs reach life safety limit state once tensile forces are created in the damaged column. In contrast, all performance levels are met in double-column damages when the column has still its compressive capacity.

An Efficient Multi-Scale Attention Feature Fusion Network for 4K Video Frame Interpolation

Video frame interpolation aims to generate intermediate frames in a video to showcase finer details. However, most methods are only trained and tested on low-resolution datasets, lacking research on 4K video frame interpolation problems. This limitation makes it challenging to handle high-frame-rate video processing in real-world scenarios. In this paper, we propose a 4K video dataset at 120 fps, named UHD4K120FPS, which contains large motion. We also propose a novel framework for solving the 4K video frame interpolation task, based on a multi-scale pyramid network structure. We introduce self-attention to capture long-range dependencies and self-similarities in pixel space, which overcomes the limitations of convolutional operations. To reduce computational cost, we use a simple mapping-based approach to lighten self-attention, while still allowing for content-aware aggregation weights. Through extensive quantitative and qualitative experiments, we demonstrate the excellent performance achieved by our proposed model on the UHD4K120FPS dataset, as well as illustrate the effectiveness of our method for 4K video frame interpolation. In addition, we evaluate the robustness of the model on low-resolution benchmark datasets.

Synthetic interpolated DSA for radiation exposure reduction via gamma variate contrast flow modeling: a retrospective cohort study

BackgroundDigital subtraction angiography (DSA) yields high cumulative radiation dosages (RD) delivered to patients. We present a temporal interpolation of low frame rate angiograms as a method to reduce cumulative RDs.MethodsPatients undergoing interventional evaluation and treatment of cerebrovascular vasospasm following subarachnoid hemorrhage were retrospectively identified. DSAs containing pre- and post-intervention runs capturing the full arterial, capillary, and venous phases with at least 16 frames each were selected. Frame rate reduction (FRR) of the original DSAs was performed to 50%, 66%, and 75% of the original frame rate. Missing frames were regenerated by sampling a gamma variate model (GVM) fit to the contrast response curves to the reduced data. A formal reader study was performed to assess the diagnostic accuracy of the “synthetic” studies (sDSA) compared to the original DSA.ResultsThirty-eight studies met inclusion criteria (average RD 1,361.9 mGy). Seven were excluded for differing views, magnifications, or motion. GVMs fit to 50%, 66%, and 75% FRR studies demonstrated average voxel errors of 2.0 ± 2.5% (mean ± standard deviation), 6.5 ± 1.5%, and 27 ± 2%, respectively for anteroposterior projections, 2.0 ± 2.2%, 15.0 ± 3.1%, and 14.8 ± 13.0% for lateral projections, respectively. Reconstructions took 0.51 s/study. Reader studies demonstrated an average rating of 12.8 (95% CI 12.3−13.3) for 75% FRR, 12.7 (12.2−13.2) for 66% FRR and 12.0 (11.5−12.5) for 50% FRR using Subjective Image Grading Scale. Kendall’s coefficient of concordance resulted in W = 0.506.ConclusionFRR by 75% combined with GVM reconstruction does not compromise diagnostic quality for the assessment of cerebral vasculature.Relevance statementUsing this novel algorithm, it is possible to reduce the frame rate of DSA by as much as 75%, with a proportional reduction in radiation exposure, without degrading imaging quality.Key points• DSA delivers some of the highest doses of radiation to patients.• Frame rate reduction (FRR) was combined with bolus tracking to interpolate intermediate frames.• This technique provided a 75% FRR with preservation of diagnostic utility as graded by a formal reader study for cerebral angiography performed for the evaluation of cerebral vasospasm.• This approach can be applied to other types of angiography studies.Graphical

Radiation reduction for interventional radiology imaging: a video frame interpolation solution

PurposeThe aim of this study was to diminish radiation exposure in interventional radiology (IR) imaging while maintaining image quality. This was achieved by decreasing the acquisition frame rate and employing a deep neural network to interpolate the reduced frames.MethodsThis retrospective study involved the analysis of 1634 IR sequences from 167 pediatric patients (March 2014 to January 2022). The dataset underwent a random split into training and validation subsets (at a 9:1 ratio) for model training and evaluation. Our approach proficiently synthesized absent frames in simulated low-frame-rate sequences by excluding intermediate frames from the validation subset. Accuracy assessments encompassed both objective experiments and subjective evaluations conducted by nine radiologists.ResultsThe deep learning model adeptly interpolated the eliminated frames within IR sequences, demonstrating encouraging peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results. The average PSNR values for angiographic, subtraction, and fluoroscopic modes were 44.94 dB, 34.84 dB, and 33.82 dB, respectively, while the corresponding SSIM values were 0.9840, 0.9194, and 0.7752. Subjective experiments conducted with experienced interventional radiologists revealed minimal discernible differences between interpolated and authentic sequences.ConclusionOur method, which interpolates low-frame-rate IR sequences, has shown the capability to produce high-quality IR images. Additionally, the model exhibits potential for reducing the frame rate during IR image acquisition, consequently mitigating radiation exposure.Critical relevance statementThis study presents a critical advancement in clinical radiology by demonstrating the effectiveness of a deep neural network in reducing radiation exposure during pediatric interventional radiology while maintaining image quality, offering a potential solution to enhance patient safety.Key points• Reducing radiation: cutting IR image to reduce radiation.• Accurate frame interpolation: our model effectively interpolates missing frames.• High visual quality in terms of PSNR and SSIM, making IR procedures safer without sacrificing quality.Graphical

Four-Bolt Unstiffened End-Plate Moment Connections with 36-in.-Deep Beams for Intermediate Moment Frames

Previous testing has shown that four-bolt extended, unstiffened end-plate moment connections can be used with beams up to 24 in. deep and develop sufficiently ductile performance to satisfy seismic qualification. It is desirable to extend this depth limit to allow deeper beams for intermediate moment frames. Three moment connection specimens using built-up 36-in.-deep beams with a four-bolt extended, unstiffened end-plate moment connection were tested to determine if they satisfy the intermediate moment frame qualification criteria given in AISC 341-16 (2016a). The web of the specimens satisfied the moderately ductile section criteria for web slenderness, which is required for intermediate moment frames, but not highly ductile section criteria required for special moment frames.
 All three specimens passed qualification criteria for intermediate moment frames (IMFs) as given in AISC 341-16 by retaining at least 80% of the nominal plastic moment strength through 2% story drift. The observed failure modes included lateral torsional buckling of the specimen, flange local buckling, net section fracture of the beam flange, and failure of the beam flange-to-beam web fillet weld. The results of these tests support current moderately ductile limits associated with lateral bracing and cross-section slenderness, given that the associated specimens reached IMF qualification criteria but not special moment frame (SMF) criteria. It was concluded that four-bolt extended, unstiffened end-plate moment connections satisfy intermediate moment frame requirements with a beam depth of 36 in. and that a modification is needed for beam web to flange welds in built-up sections near the moment connection.

Dynamic Healing Process Analysis: Image Morphing with Warping Technique for Nose and Esophagus Studies

In this study, image morphing technology is harnessed to investigate the dynamic processes of healing in both the nose and esophagus. The researchers employ a computer-assisted morphing approach to dynamically analyze mucosal processes during the healing study. The focal point of the proposed image morphing method is a specified warping technique that transforms the first image into the second, and vice versa. The metamorphosis unfolds with the first image initially completely deformed toward the second, gradually fading in, while the current image simultaneously becomes progressively distorted and fades out. This results in an initial series of photographs closely resembling the original. The pivotal middle image is crafted through the averaging of the first source image altered halfway toward the second and the second source picture deformed halfway away from the first. The subsequent images in the series resemble the second source picture. The significance of the center picture is underscored, as its visual appeal ensures the overall aesthetic quality of the entire animation sequence. To facilitate seamless transitions between frames, intermediate frames are generated. The proposed technique is substantiated through the use of realistic datasets for testing and validation, demonstrating its superior performance compared to state-of-the-art studies in the field. This comprehensive approach to image morphing not only advances our understanding of healing dynamics in the nose and esophagus but also presents a novel and effective methodology with broad applicability, surpassing the capabilities of current leading-edge studies. The proposed approach showed best performance against state-of-the works

Fast-Vid2Vid++: Spatial-Temporal Distillation for Real-Time Video-to-Video Synthesis.

Video-to-Video synthesis (Vid2Vid) gains remarkable performance in generating a photo-realistic video from a sequence of semantic maps, such as segmentation, sketch and pose. However, this pipeline is heavily limited to high computational cost and long inference latency, mainly attributed to two essential factors: 1) network architecture parameters, 2) sequential data stream. Recently, the parameters of image-based generative models have been significantly reduced via more efficient network architectures. Existing methods mainly focus on slimming network architectures but ignore the size of the sequential data stream. Moreover, due to the lack of temporal coherence, image-based compression is not sufficient for the compression of the video task. In this paper, we present a spatial-temporal hybrid distillation compression framework, Fast-Vid2Vid++, which focuses on knowledge distillation of the teacher network and the data stream of generative models on both space and time. Fast-Vid2Vid++ makes the first attempt at time dimension to transfer hierarchical features and time coherence knowledge to reduce computational resources and accelerate inference. Specifically, we compress the data stream spatially and reduce the temporal redundancy. We distill the knowledge of the hierarchical features and the final response from the teacher network to the student network in high-resolution and full-time domains. We transfer the long-term dependencies of the features and video frames to the student model. After the proposed spatial-temporal hybrid knowledge distillation (Spatial-Temporal-HKD), our model can synthesize high-resolution key-frames using the low-resolution data stream. Finally, Fast-Vid2Vid++ interpolates intermediate frames by motion compensation with slight latency and generates full-length sequences with motion-aware inference (MAI). On standard benchmarks, Fast-Vid2Vid++ achieves a real-time performance of 30-59 FPS and saves 28-35× computational cost on a single V100 GPU. Code and models are publicly available.

Edge-Aware Network for Flow-Based Video Frame Interpolation.

Video frame interpolation can up-convert the frame rate and enhance the video quality. In recent years, although interpolation performance has achieved great success, image blur usually occurs at object boundaries owing to the large motion. It has been a long-standing problem and has not been addressed yet. In this brief, we propose to reduce the image blur and get the clear shape of objects by preserving the edges in the interpolated frames. To this end, the proposed edge-aware network (EA-Net) integrates the edge information into the frame interpolation task. It follows an end-to-end architecture and can be separated into two stages, i.e., edge-guided flow estimation and edge-protected frame synthesis. Specifically, in the flow estimation stage, three edge-aware mechanisms are developed to emphasize the frame edges in estimating flow maps, so that the edge maps are taken as auxiliary information to provide more guidance to boost the flow accuracy. In the frame synthesis stage, the flow refinement module is designed to refine the flow map, and the attention module is carried out to adaptively focus on the bidirectional flow maps when synthesizing the intermediate frames. Furthermore, the frame and edge discriminators are adopted to conduct the adversarial training strategy, so as to enhance the reality and clarity of synthesized frames. Experiments on three benchmarks, including Vimeo90k, UCF101 for single-frame interpolation, and Adobe240-fps for multiframe interpolation, have demonstrated the superiority of the proposed EA-Net for the video frame interpolation task.

Strong column-weak beam relationship of 3D steel joints with tubular columns: Assessment, Validation and Design proposal

The study of moment connections in steel structures subjected to cyclic loads has been extensively studied, providing a great number of requirements, including the strong column-weak beam relationship, to guarantee a satisfactory cyclic performance. However, investigations on the cyclic performance of moment connections considering the bidirectional and axial load effects simultaneously with tubular columns are limited. This study aims to assess and validate the strong column-weak beam relationship of 3D steel moment connections using reduced order models. The simplified model (reduced order model) approach was employed to extend the range of beam and column elements sizes and reduce the experimental and computational costs. These models were calibrated from full-scale experimental studies. A great number of configurations with different beam and column sizes without loss of reliability and structural representativeness of the studied phenomenon were studied. A total of 13640 simplified models were developed. Results show a cyclic behavior controlled by the strong column-weak beam relationship to modify the joint's failure mechanism. The increasing of strong column-weak beam relationship and the biaxial effect caused degradation of the strength and stiffness as well as in dissipated energy. An optimal strong column-weak beam relationship was obtained for all joint configurations analyzed. Finally, a robust design procedure is proposed, ensuring the cyclic behavior of end-plate moment connection with built-up box column including biaxial effect and axial load. Therefore, the use of this type of moment connection can be used in special and intermediate moment frames designed according to Seismic provisions.

Experimental and numerical study on weld strengths of built-up steel box columns

This study investigates the demands generated in the panel zone of welded steel built-up box columns with fillet and complete joint penetration (CJP) groove welds, which are mainly used as columns in steel moment-resisting frames. The motivation for this study stems from the a 360–16 specification for structural steel building, which requires the joining of flange and web plates of box columns with CJP groove welds within a zone extending 12 in. above the upper beam flange and 12 in. below the lower beam flange in special and intermediate steel moment frames. Laboratory experiments have been conducted to examine the strength of these welds under pure shear forces. For this purpose, two laboratory specimens, one with fillet welds and the other with CJP groove welds, have been designed, and the strength of these welds were equal to each other in pure shear force conditions. The fillet weld specimen has resisted up to 1670 kN in the pure shear test, and the CJP groove weld has resisted up to 1520 kN. The entire loading process of each specimen was characterized by employing the digital image correlation (DIC) technique, which enabled the extraction of strain values throughout. Numerical modeling is validated in Abaqus software using experimental results, with comprehensive analyses on force-displacement diagrams and rupture. The outcomes of the tests conducted have shown that the demands generated in the panel zone of the built-up steel box welded sections are satisfactorily tolerated by both fillet and CJP groove welds. Research significanceThe significance of this study can be elaborated in multiple aspects. Firstly, it introduces an innovative test setup meticulously designed to replicate the behavior of welded built-up box columns with corner joints to steel plates. This advanced experimental arrangement not only facilitates the current study, but also serves as a versatile tool for conducting forthcoming experiments, allowing for meaningful comparisons and further exploration of diverse phenomena within the field. Secondly, the deliberate imposition of uniform demands on both types of welds during the study enables a comprehensive examination of their behavioral characteristics. This comprehensive analysis contributes to a more profound comprehension of welds performance under identical conditions, thereby enhancing our knowledge within the field. Finally, the research outcome, which reveals that fillet welds exhibit almost identical load-bearing capacity in comparison to CJP groove welds, bears substantial practical significance for the construction industry. This finding has the potential to instigate more cost-effective and efficient welding practices, which are paramount for maintaining the structural integrity of steel structures.In summary, this study makes a noteworthy contribution to the scientific community by presenting an innovative test setup, deepening the knowledge of weld performance, and suggesting practical enhancements in welding methodologies. These collective contributions serve to elevate the field of structural engineering, promoting advancements in construction practices.

Emulation Evaluation of Interior Beam-Column Connections in PC and RC Moment-Resisting Frames.

Precast concrete (PC) structures have many advantages, but their use in the construction of middle- to high-rise buildings is limited. The construction of PC structures requires skills in various operations such as transportation, assembly, lifting, and structural soundness. In particular, regarding the seismic design of PC structures, it is necessary to clearly evaluate whether they have the same structural performance and usability as integral RC (cast-in-place) structures. In this paper, an experimental study was conducted to investigate whether PC members can achieve a seismic performance equivalent to that of RC members in beam-column joints, which are representative moment-resisting frames. The main variables are the two types of structural systems (intermediate and special moment-resisting frames) and the design flexural strength ratio of the columns and beams. The experimental and analytical results showed that the seismic performance of the PC specimens was equivalent to that of the RC specimens in terms of strength, stiffness, energy dissipation, and strain distribution, except for the specimen with splice sleeve bond failure of the column reinforcement (poor filling of the internal mortar). In addition, the I series satisfied the present emulation evaluation criteria for special moment-resisting frames of PC structures, confirming the possibility of applying intermediate moment-resisting frames.

Efficient Interpolation of Rough Line Drawings

AbstractIn traditional 2D animation, sketches drawn at distant keyframes are used to design motion, yet it would be far too labor‐intensive to draw all the inbetween frames to fully visualize that motion. We propose a novel efficient interpolation algorithm that generates these intermediate frames in the artist's drawing style. Starting from a set of registered rough vector drawings, we first generate a large number of candidate strokes during a pre‐process, and then, at each intermediate frame, we select the subset of those that appropriately conveys the underlying interpolated motion, interpolates the stroke distributions of the key drawings, and introduces a minimum amount of temporal artifacts. In addition, we propose quantitative error metrics to objectively evaluate different stroke selection strategies. We demonstrate the potential of our method on various animations and drawing styles, and show its superiority over competing raster‐ and vector‐based methods.