Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural language instructions. By carefully integrating neural scene modeling, LLM-based code generation, and physical simulation, AutoVFX is able to provide physically-grounded, photorealistic editing effects that can be controlled directly using natural language instructions. We conduct extensive experiments to validate AutoVFX's efficacy across a diverse spectrum of videos and instructions. Quantitative and qualitative results suggest that AutoVFX outperforms all competing methods by a large margin in generative quality, instruction alignment, editing versatility, and physical plausibility.

We present UrbanIR (Urban Scene Inverse Rendering), a new inverse graphics model that enables realistic, free-viewpoint renderings of scenes under various lighting conditions with a single video. It accurately infers shape, albedo, visibility, and sun and sky illumination from wide-baseline videos, such as those from car-mounted cameras, differing from NeRF's dense view settings. In this context, standard methods often yield subpar geometry and material estimates, such as inaccurate roof representations and numerous ‘floaters’. UrbanIR addresses these issues with novel losses that reduce errors in inverse graphics inference and rendering artifacts. Its techniques allow for precise shadow volume estimation in the original scene. The model's outputs support controllable editing, enabling photorealistic free-viewpoint renderings of night simulations, relit scenes, and inserted objects, marking a significant improvement over existing state-of-the-art methods. Our code and data will be made publicly available upon acceptance.

Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport, but relies on precise scene representations–explicit 3D geometry, high-quality material properties, and lighting conditions–that are often impractical to obtain in real-world scenarios. Therefore, we introduce DiffusionRenderer, a neural approach that addresses the dual problem of inverse and forward rendering within a holistic framework. Leveraging powerful video diffusion model priors, the inverse rendering model accurately estimates G-buffers from real-world videos, providing an interface for image editing tasks, and training data for the rendering model. Conversely, our rendering model generates photorealistic images from G-buffers without explicit light transport simulation. Specifically, we first train a video diffusion model for inverse rendering on synthetic data, which generalizes well to real-world videos and allows us to auto-label diverse real-world videos. We then co-train our rendering model using both synthetic and auto-labeled real-world data. Experiments demonstrate that DiffusionRenderer effectively approximates inverse and forwards rendering, consistently outperforming the state-of-the-art. Our model enables practical applications from a single video input—including relighting, material editing, and realistic object insertion.

Inverse rendering seeks to recover 3D geometry, surface material, and lighting from captured images, enabling advanced applications such as novel-view synthesis, relighting, and virtual object insertion. However, most existing techniques rely on high dynamic range (HDR) images as input, limiting accessibility for general users. In response, we introduce IRIS, an inverse rendering framework that recovers the physically based material, spatially-varying HDR lighting, and camera response functions from multi-view, low-dynamic-range (LDR) images. By eliminating the dependence on HDR input, we make inverse rendering technology more accessible. We evaluate our approach on real-world and synthetic scenes and compare it with state-of-the-art methods. Our results show that IRIS effectively recovers HDR lighting, accurate material, and plausible camera response functions, supporting photorealistic relighting and object insertion.

We present InvRGB+L, a novel inverse rendering model that reconstructs large, relightable, and dynamic scenes from a single RGB+LiDAR sequence. Conventional inverse graphics methods rely primarily on RGB observations and use LiDAR mainly for geometric information, often resulting in suboptimal material estimates due to visible light interference. We find that LiDAR's intensity values-captured with active illumination in a different spectral range-offer complementary cues for robust material estimation under variable lighting. Inspired by this, InvRGB+L leverages LiDAR intensity cues to overcome challenges inherent in RGB-centric inverse graphics through two key innovations: (1) a novel physics-based LiDAR shading model and (2) RGB-LiDAR material consistency losses. The model produces novel-view RGB and LiDAR renderings of urban and indoor scenes and supports relighting, night simulations, and dynamic object insertions, achieving results that surpass current state-of-the-art methods in both scene-level urban inverse rendering and LiDAR simulation.

Digitizing the physical world into accurate simulation‑ready virtual environments offers significant opportunities in a variety of fields such as augmented and virtual reality, gaming, and robotics. However, current 3D reconstruction and scene-understanding methods commonly fall short in one or more critical aspects, such as geometry completeness, object interactivity, physical plausibility, photorealistic rendering, or realistic physical properties for reliable dynamic simulation. To address these limitations, we introduce HoloScene, a novel interactive 3D reconstruction framework that simultaneously achieves these requirements. HoloScene leverages a comprehensive interactive scene-graph representation, encoding object geometry, appearance, and physical properties alongside hierarchical and inter-object relationships. Reconstruction is formulated as an energy-based optimization problem, integrating observational data, physical constraints, and generative priors into a unified, coherent objective. Optimization is efficiently performed via a hybrid approach combining sampling-based exploration with gradient-based refinement. The resulting digital twins exhibit complete and precise geometry, physical stability, and realistic rendering from novel viewpoints. Evaluations conducted on multiple benchmark datasets demonstrate superior performance, while practical use-cases in interactive gaming and real-time digital-twin manipulation illustrate HoloScene's broad applicability and effectiveness.

Blockchain has been widely used in monetary systems, hence its consistency under all circumstances is a serious concern. Since it can be damaged physically and virtually, it is crucial to develop a protection mechanism that binds both overlay and underlay networks. This paper focuses on the functionality of blockchain under attacks. We propose a heuristic algorithm to enhance the reliability of information propagation. First, for underlay, we scrutinize the performance of the network during a partial malfunction. We develop a series of mathematical formulas to construct the communication tree model and evaluate its delay. Second, in the overlay, we add the trustworthiness of each node as a parameter. It is an indicator used in path selection to avoid malicious tampering. Our purposed method strengthens the tie between both physical and virtual networks so that blockchain thrives even in abnormal situations. It greatly increases the resilience of blockchain and provides a reliable system for digital transactions.

Abstract The paper investigates resource allocation via power control for inter‐cell interference (ICI) mitigation in an orthogonal frequency division multiple access‐based cellular network. The proposed scheme is featured by a novel subcarrier assignment mechanism at a central controller for ICI, which is further incorporated with an intelligent power control scheme. We formulate the system optimization task into a constrained optimization problem for maximizing accepted users' requirements. To improve the computation efficiency, a fast yet effective heuristic approach is introduced for divide and conquer. Simulation results demonstrate that the proposed resource allocation scheme can significantly improve the network capacity compared with a common approach by frequency reuse. Copyright © 2014 John Wiley & Sons, Ltd.

ABSTRACT In recent years, cloud computing has become popular and its applications widespread. Thus, there exists a common concern, that is, how to arrange and monitor various resources in the cloud computing environment. In the literature, Ganglia and Network Weather Service (NWS) were used to monitor and gather node status and network‐related data, respectively. With supports of Ganglia and NWS, one can effectively administer available resources in the cloud computing environment. In order to achieve high performance of cloud computing, comprehensive monitoring and efficient management are critical. Ganglia is often used to gather status data of resources, such as live states of hosts, CPU or memory utilizations, and surely Ganglia is also capable of monitoring network‐related information; however, instead of Ganglia, we used NWS services to gather network‐related information such as end‐to‐end transmission control protocol/Internet protocol performance data. Compared with Ganglia, NWS services offer more selections and flexibility for measurement schemes. Besides, NWS services could be deployed with nonintruding manner that makes it easier and faster in deploying services to cloud nodes. The network‐related information is acquired immediately after deployment. Although NWS services also provide measurements for CPU and memory utilizations, but less functionality is provided by them than Ganglia in these aspects. Therefore, we combine advantageous features of Ganglia and NWS to achieve the aims of effective monitoring and management of available resources in the cloud environment. Nevertheless, Ganglia and NWS services may not provide sufficient data in realistic situations due to diversified needs of users, especially application developers. For instance, users are not able to directly access utilizations or allocations of resources in the cloud environment via interfaces or channels of Ganglia or NWS. In addition, NWS services based on a domain‐based network information model could greatly decrease overheads caused by unnecessary measurements. Hence, we propose a heuristic QoS measurement approach based on the domain‐based information model. This measurement approach is capable of providing essential information to satisfy user requirements, and thus let users manage and monitor various resources in the cloud environment in a more efficient way. © 2013 Wiley Periodicals, Inc.

In this paper, a trajectory tracking control strategy by elliptic collision-Free approach is proposed for guiding and following mobile robots. Each robot is setup with a single eye camera to capture the image of a guiding robot or obstacle. The image between them is converted to a gray form, and then to the gradient for each pixel of Gaussian pyramid level to estimate the motion parameters, disturbances, or obstacles. Successive images for a moving object are used to estimate or recognize the foreground, guiding robot, and obstacles by calculating the gravity of the moving object, and then the following mobile robot will follow the trajectory of the guiding one. On the contrary, the following mobile robot turns out to serve as a guiding one when there is no gravity. Furthermore, the elliptic collision-free path will be used for obstacle gravity and keep going until the final destination is achieved. Finally, experimental results will be used to show the effectiveness of the proposed method.

We study the problems of resource and performance management in channelized wireless networks. For identifying research issues systematically, we propose a research framework which consists of two modules: performance optimization and network servicing modules. For an in-service communication network, performance optimization module manages radio resources to ensure communication quality of service and optimize system performance. For channelized wireless system, the real-time issues of performance optimization are admission control, channel assignment, power control, and homing. The network servicing module is event-driven or a periodically resource augmentation plan, which alleviates the performance exceptions and optimizes long-term system revenue by using corrective mechanisms consisting of resource augmentation, channel reassignment, cell rearrangement, and rehoming issues. Under the consideration of generic sectorization and interference effects, we introduce several mathematical models for both modules and formulate the associated issues as combinatorial integer programming problems. By using the Lagrangean relaxation method, several efficient and effective algorithms are developed to deal with these NP-hard problems.