Simulating event streams from 3D scenes has become a common practice in event-based vision research, as it meets the demand for large-scale, high temporal frequency data without setting up expensive hardware devices or undertaking extensive data collections. Yet existing methods in this direction typically work with noiseless RGB frames that are costly to render, and therefore their simulations are often unrealistically low in temporal resolution. In this work, we propose EventTracer, a path tracing-based rendering pipeline that simulates high-fidelity event sequences from complex 3D scenes in an efficient and physics-aware manner. Specifically, we speed up the rendering process via low sample-per-pixel (SPP) path tracing, and train a lightweight event spiking network to denoise the resulting RGB videos into realistic event sequences. Our EventTracerpipeline runs at a speed of $\sim$∼1 minutes per second of 360p video, and it inherits the merit of accurate spatiotemporal modeling from its path tracing backbone. We show through the Real2Sim and Sim2Real tests that EventTracercaptures higher-fidelity scene details and demonstrates a greater similarity to real-world event data than alternative event simulators, which establishes it as a potential tool for creating large-scale event-RGB datasets, narrowing the sim-to-real gap in event-based vision, and boosting various downstream applications.

High-fidelity 3D video reconstruction is essential for enabling real-time rendering of dynamic scenes with realistic motion in VR/AR. The deformation field paradigm of 3D Gaussian splatting has achieved near-photorealistic results in video reconstruction due to the great representation capability of deep deformation networks. However, in videos with complex motion and significant scale variations, deformation networks often overfit to irregular Gaussian trajectories, leading to suboptimal visual quality. Moreover, the gradient-based densification strategy designed for static scene reconstruction proves inadequate to address the absence of dynamic content. In light of these challenges, we propose a flow-empowered velocity field modeling scheme tailored for Gaussian video reconstruction, dubbed FlowGaussian-VR. It consists of two core components: a velocity field rendering (VFR) pipeline which enables optical flow-based optimization, and a flow-assisted adaptive densification (FAD) strategy that adjusts the number and size of Gaussians in dynamic regions. We also explore a temporal velocity refinement (TVR) post-processing algorithm to further estimate and correct noise in Gaussian trajectories via extended Kalman filtering. We validate our model's effectiveness on multi-view dynamic reconstruction and novel view synthesis with real-world datasets containing challenging motion scenarios, demonstrating not only notable visual improvements (over 2.5 dB gain in PSNR) and less blurry artifacts in dynamic textures, but also regularized and trackable per-Gaussian trajectories.

Scratch-represented 3D visual arts can create compelling visual effects by manipulating light reflections across surfaces. Established works, such as those involving scratch holograms, have realized impressive multi-view imagery effects of reflection arts. However, creating a continuous view of 3D virtual objects with shading effects, especially view-dependent shading remains a challenge. Yet, most reported works are demonstrated on planar surfaces, leaving exploring the potential benefits of leveraging curved surfaces for diverse imagery scenarios an interesting research avenue. This work explores the continuous view-dependent imagery with rich shading effects via scratch-based reflection, whose design space has the potential to be extended to arbitrary curved surfaces. This is achieved by solving the ordinary differential equations under constraints calculated from established bidirectional reflectance distribution function models to optimize scratch distribution on substrate surfaces. Importantly, we create real-world examples by manufacturing optimized reflectors using off-the-shelf carving machines, delivering state-of-the-art specular view-dependent imagery that features continuous and realistic shading effects on both planar and developable curved surfaces.

Rumors are widely spread on social media, which has a negative impact on social stability. To address this problem, many rumor detection methods have been proposed. However, most existing methods overlook the potential impact of noise and adversarial attacks on their detection performance, which could compromise their effectiveness when applied in an unknown environment. To overcome these challenges and improve the framework robustness to noise and adversarial attacks, we propose a novel rumor detection framework with Adaptive Data Augmentation and Adversarial Training, named ADAAT. Our framework utilizes the adaptive data augmentation module to calculate the importance of edges and features and adaptively modify the less important among them with a greater probability. In addition, it contains a hard sample generation module which generates adversarial representations through adversarial training. These adversarial representations are treated as hard samples, which are utilized in contrastive learning to learn essential features, thereby improving the robustness of the framework. Our framework proves superiority in rumor detection tasks, increasing the accuracy by an average of 3.6%, 4.5% and 2.5% over the state-of-the-art methods on Twitter15, Twitter16 and PHEME, respectively. When the ADAAT framework is applied to attacked test data, the detection accuracy decreases by only 1.3%, 1.4%, and 1.2%. This paper appears in the AI & Society Track.

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users’ delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical communication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

Transfer learning (TL) is a powerful approach that enhances the generalizability of cross-domain fault diagnosis. However, the challenge of acquiring high-quality mechanical fault signals limits its application. This article introduces the extreme class imbalance problem in the cross-domain diagnosis, restricting the label space of the target domain while relaxing the restrictions of unsupervised learning. The study proposes a novel generative TL method called fast sparse neural style, which employs sparse representation to capture the domain-invariant fault features as well as the Gram matrix to measure the domain-specific features. Fault features and domain features are proven to be separable in mechanical signals and are fused in the data generation process. Compared to other methods through various cross-domain diagnostic tasks on a piston aero-engine, the proposed method has obvious advantages in tasks with substantial inter-domain differences, demonstrating the potential and research value of generative TL.

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of Communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical commu-nication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

As artificial intelligence (AI) and machine learning continue to advance, they have become key players in signal analysis and pattern recognition. However, challenges such as domain shift and a lack of labeled data hinder the application of machine learning to new, unfamiliar signals, particularly in the development of intelligent diagnostic methods for mechanical faults. In this study, we address these challenges by framing them as the extreme class imbalance problem. We introduce a novel generative transfer learning framework that leverages cycle-consistent adversarial networks with hard constraints (CycleGAN-HCs). This framework is designed to generate unpaired mechanical fault signals, which are then used to train classifiers that can operate across different domains. To enhance feature extraction capabilities for time series data, we developed a new generator model called U-Attention. Additionally, we refined the training loss function to better capture domain-specific and classification-specific features in mechanical vibration signals. The proposed method successfully facilitates feature transfer and data augmentation. Its effectiveness and reliability have been demonstrated through four cross-domain fault diagnosis tasks involving piston aero-engines. Comparative verification shows that the generative transfer learning diagnostic framework outperforms traditional methods, offering superior performance and better generalization in complex mechanical fault diagnosis scenarios.

The 3D visual optical arts create fascinating special effects by carefully designing interactions between objects and light sources. One of the essential types is 3D reflection art, which aims to create reflectors that can display different images when viewed from different directions. Existing works produce impressive visual effects. Unfortunately, previous works discretize the reflector surface with regular grids/facets, leading to a large parameter space and a high optimization time cost. In this paper, we introduce a new type of 3D reflection art -scratch-based reflection art, which allows for a more compact parameter space, easier fabrication, and computationally efficient optimization. To design a 3D reflection art with scratches, we formulate it as a multi-view optimization problem and introduce differentiable rendering to enable efficient gradient-based optimizers. For that, we propose an analytical scratch rendering approach, together with a high-performance rendering pipeline, allowing efficient differentiable rendering. As a consequence, we could display multiple images on a single metallic board with only several minutes for optimization. We demonstrate our work by showing virtual objects and manufacturing our designed reflectors with a carving machine.

Abstract Path tracing has been used for real‐time renderings, thanks to the powerful GPU device. Unfortunately, path tracing produces noisy rendered results, thus, filtering or denoising is often applied as a post‐process to remove the noise. Previous works produce high‐quality denoised results, by accumulating the temporal samples. However, they cannot handle the details from bidirectional reflectance distribution function (BRDF) maps (e.g. roughness map). In this paper, we introduce the BRDF pre‐integration factorization for denoising to better preserve the details from BRDF maps. More specifically, we reformulate the rendering equation into two components: the BRDF pre‐integration component and the weighted‐lighting component. The BRDF pre‐integration component is noise‐free, since it does not depend on the lighting. Another key observation is that the weighted‐lighting component tends to be smooth and low‐frequency, which indicates that it is more suitable for denoising than the final rendered image. Hence, the weighted‐lighting component is denoised individually. Our BRDF pre‐integration demodulation approach is flexible for many real‐time filtering methods. We have implemented it in spatio‐temporal variance‐guided filtering (SVGF), ReLAX and ReBLUR. Compared to the original methods, our method manages to better preserve the details from BRDF maps, while both the memory and time cost are negligible.