ACM MM’25: SVD: Spatial Video Dataset

SVD: Spatial Video Dataset

ACM Multimedia 2025

October 27 – October 31, 2025

Dublin, Ireland

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MH Izadimehr, Milad Ghanbari, Guodong Chen, Wei Zhou, Xiaoshuai Hao, Mallesham Dasari, Christian Timmerer, Hadi Amirpour

Abstract:  Stereoscopic video has long been the subject of research due to its ability to deliver immersive three-dimensional content to a wide range of applications, from virtual and augmented reality to advanced human–computer interaction. The dual‑view format inherently provides binocular disparity cues that enhance depth perception and realism, making it indispensable for fields such as telepresence, 3D mapping, and robotic vision. Until recently, however, end‑to‑end pipelines for capturing, encoding, and viewing high‑quality 3D video were neither widely accessible nor optimized for consumer‑grade devices. Today’s smartphones, such as the iPhone Pro and modern HMDs like the AVP, offer built‑in support for stereoscopic video capture, hardware‑accelerated encoding, and seamless playback on devices like the AVP and Meta Quest 3, which require minimal user intervention. Apple refers to this streamlined workflow as spatial Video. Making the full stereoscopic video process available to everyone has made new applications possible. Despite these advances, there remains a notable absence of publicly available datasets that include the complete spatial video pipeline on consumer platforms, hindering reproducibility and comparative evaluation of emerging algorithms.

In this paper, we introduce SVD, a spatial video dataset comprising 300 five-second video sequences, i.e., 150 captured using an iPhone Pro and 150 with an AVP. Additionally, 10 longer videos with a minimum duration of 2 minutes have been recorded. The SVD is publicly released under an open source license to facilitate research in codec performance evaluation, subjective and objective Quality of Experience assessment, depth‑based computer vision, stereoscopic video streaming, and other emerging 3D applications such as neural rendering and volumetric capture. Link to the dataset: https://cd-athena.github.io/SVD/.

 

ACM MM’25: Nature-1k: The Raw Beauty of Nature in 4K at 60FPS

Nature-1k: The Raw Beauty of Nature in 4K at 60FPS

ACM Multimedia 2025

October 27 – October 31, 2025

Dublin, Ireland

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Mohammad Ghasempour (AAU, Austria), Hadi Amirpour (AAU, Austria), Christian Timmerer (AAU, Austria)

Abstract: The push toward data-driven video processing, combined with recent advances in video coding and streaming technologies, has fueled the need for diverse, large-scale, and high-quality video datasets. However, the limited availability of such datasets remains a key barrier to the development of next-generation video processing solutions. In this paper, we introduce Nature-1k, a large-scale video dataset consisting of 1000 professionally captured 4K Ultra High Definition (UHD) videos, each recorded at 60fps. The dataset covers a wide range of environments, lighting conditions, texture complexities, and motion patterns. To maintain temporal consistency, which is crucial for spatio-temporal learning applications, the dataset avoids scene cuts within the sequences. We further characterize the dataset using established metrics, including spatial and temporal video complexity metrics, as well as colorfulness, brightness, and contrast distribution. Moreover, Nature-1k includes a compressed version to support rapid prototyping and lightweight testing. The quality of the compressed videos is evaluated using four commonly used video quality metrics: PSNR, SSIM, MS-SSIM, and VMAF. Finally, we compare Nature-1k with existing datasets to demonstrate its superior quality and content diversity. The dataset is suitable for a wide range of applications, including Generative Artificial Intelligence (AI), video super-resolution and enhancement, video interpolation, as well as video coding, and adaptive video streaming optimization. Dataset URL: Link

Receiving Kernel-Level Insights via eBPF: Can ABR Algorithms Adapt Smarter?

Receiving Kernel-Level Insights via eBPF: Can ABR Algorithms Adapt Smarter?

Würzburg Workshop on Next-Generation Communication Networks (WueWoWAS) 2025

6 – 8 Oct 2025, Würzburg, Germany

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Mohsen Ghasemi (Sharif University of Technology, Iran); Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt, Austria); Mahdi Dolati (Sharif University of Technology, Iran); Farzad Tashtarian (Alpen-Adria Universität Klagenfurt, Austria); Sergey Gorinsky (IMDEA Networks Institute, Spain); Christian Timmerer (Alpen-Adria-Universität Klagenfurt & Bitmovin, Austria)

Abstract: The rapid rise of video streaming services such as Netflix and YouTube has made video delivery the largest driver of global Internet traffic, including mobile networks such as 5G or the upcoming 6G network. To maintain playback quality, client devices employ Adaptive Bitrate (ABR) algorithms that adjust video quality based on metrics like available bandwidth and buffer occupancy. However, these algorithms often react slowly to sudden bandwidth fluctuations due to limited visibility
into network conditions, leading to stall events that significantly degrade the user’s Quality of Experience (QoE). In this work, we introduce CaBR, a Congestion-aware adaptive BitRate decision module designed to operate on top of existing ABR algorithms. CaBR enhances video streaming performance by leveraging real-time, in-kernel network telemetry collected via the extended Berkeley Packet Filter (eBPF). By utilizing congestion metrics such as queue lengths observed at network switches, CaBR refines the bitrate selection of the underlying ABR algorithms for upcoming segments, enabling faster adaptation to changing network conditions. Our evaluation shows that CaBR significantly reduces the playback stalls and improves QoE by up to 25% compared to state-of-the-art approaches in a congested environment.

 

Hadi

BMVC’25: Cross-Modal Scene Semantic Alignment for Image Complexity Assessment

Cross-Modal Scene Semantic Alignment for Image Complexity Assessment

British Machine Vision Conference (BMVC) 2025

November, 2025

Sheffield, UK

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Yuqing Luo, YIXIAO LI, Jiang Liu, Jun Fu, Hadi Amirpour, Guanghui Yue, Baoquan Zhao, Padraig Corcoran, Hantao Liu, Wei Zhou

Abstract: Image complexity assessment (ICA) is a challenging task in perceptual evaluation due to the subjective nature of human perception and the inherent semantic diversity in real-world images. Existing ICA methods predominantly rely on hand-crafted or shallow convolutional neural network-based features of a single visual modality, which are insufficient to fully capture the perceived representations closely related to image complexity. Recently, cross-modal scene semantic information has been shown to play a crucial role in various computer vision tasks, particularly those involving perceptual understanding. However, the exploration of cross-modal scene semantic information in the context of ICA remains unaddressed. Therefore, in this paper, we propose a novel ICA method called Cross-Modal Scene Semantic Alignment (CM-SSA), which leverages scene semantic alignment from a cross-modal perspective to enhance ICA performance, enabling complexity predictions to be more consistent with subjective human perception. Specifically, the proposed CM-SSA consists of a complexity regression branch and a scene semantic alignment branch. The complexity regression branch estimates image complexity levels under the guidance of the scene semantic alignment branch, while the scene semantic alignment branch is used to align images with corresponding text prompts that convey rich scene semantic information by pair-wise learning. Extensive experiments on several ICA datasets demonstrate that the proposed CM-SSA significantly outperforms state-of-the-art approaches.

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ACM MM’25 Open Source: diveXplore – An Open-Source Software for Modern Video Retrieval with Image/Text Embeddings

diveXplore – An Open-Source Software for Modern Video Retrieval with Image/Text Embeddings

ACM Multimedia 2025

October 27 – October 31, 2025

Dublin, Ireland

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Mario Leopold (AAU, Austria), Farzad Tashtarian (AAU, Austria), Klaus Schöffmann (AAU, Austria)

Abstract:Effective video retrieval in large-scale datasets presents a significant challenge, with existing tools often being too complex, lacking sufficient retrieval capabilities, or being too slow for rapid search tasks. This paper introduces diveXplore, an open-source software designed for interactive video retrieval. Due to its success in various competitions like the Video Browser Showdown (VBS) and the Interactive Video Retrieval 4 Beginners (IVR4B), as well as its continued development since 2017, diveXplore is a solid foundation for various kinds of retrieval tasks. The system is built on a three-layer architecture, comprising a backend for offline preprocessing, a middleware with a Node.js and Python server for query handling, and a MongoDB for metadata storage, as well as an Angular-based frontend for user interaction. Key functionalities include free-text search using natural language, temporal queries, similarity search, and other specialized search strategies. By open-sourcing diveXplore, we aim to establish a solid baseline for future research and development in the video retrieval community, encouraging contributions and adaptations for a wide range of use cases, even beyond competitive settings.

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Paper Accepted: GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media

GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media

ACM Multimedia 2025

October 27 – October 31, 2025

Dublin, Ireland

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Emanuele Artioli (AAU, Austria), Daniele Lorenzi (AAU, Austria), Shivi Vats (AAU, Austria), Farzad Tashtarian (AAU, Austria), Christian Timmerer (AAU, Austria)

Abstract: Video streaming dominates global internet traffic, yet conventional pipelines remain inefficient for structured, human-centric content such as sports, performance, or interactive media. Standard codecs re-encode entire frames, foreground and background alike, treating all pixels uniformly and ignoring the semantic structure of the scene. This leads to significant bandwidth waste, particularly in scenarios where backgrounds are static and motion is constrained to a few salient actors. We introduce GenStream, a semantic streaming framework that replaces dense video frames with compact, structured metadata. Instead of transmitting pixels, GenStream encodes each scene as a combination of skeletal keypoints, camera viewpoint parameters, and a static 3D background model. These elements are transmitted to the client, where a generative model reconstructs photorealistic human figures and composites them into the 3D scene from the original viewpoint. This paradigm enables extreme compression, achieving over 99.9% bandwidth reduction compared to HEVC. We partially validate GenStream on Olympic figure skating footage and demonstrate potential high perceptual fidelity under minimal data. Looking forward, GenStream opens new directions in volumetric avatar synthesis, canonical 3D actor fusion across views, personalized and immersive viewing experiences at arbitrary viewpoints, and lightweight scene reconstruction, laying the groundwork for scalable, intelligent streaming in the post-codec era.

Paper Accepted: EuroXR 2025 Demo: STEP-MR: A Subjective Testing and Eye-Tracking Platform for Dynamic Point Clouds in Mixed Reality

Paper Title: STEP-MR: A Subjective Testing and Eye-Tracking Platform for Dynamic Point Clouds in Mixed Reality

Conference Details:  EuroXR 2025; Sep 03 – Sep 05, 2025; Winterthur, Switzerland

Authors: Shivi Vats (AAU, Austria), Christian Timmerer (AAU, Austria), Hermann Hellwagner (AAU, Austria)

Abstract: 

The use of point cloud (PC) streaming in mixed reality (MR) environments is of particular interest due to the immersiveness and the six degrees of freedom (6DoF) provided by the 3D content. However, this immersiveness requires significant bandwidth. Innovative solutions have been developed to address these challenges, such as PC compression and/or spatially tiling the PC to stream different portions at different quality levels. This paper presents a brief overview of a Subjective Testing and Eye-tracking Platform for dynamic point clouds in Mixed Reality (STEP-MR) for the Microsoft HoloLens 2. STEP-MR was used to conduct subjective tests (described in another work) with 41 participants, yielding over 2000 responses and more than 150 visual attention maps, the results of which can be used, among other things, to improve dynamic (animated) point cloud streaming solutions mentioned above. Building on our previous platform, the new version now enables eye-tracking tests, including calibration and heatmap generation. Additionally, STEP-MR features modifications to the subjective tests’ functionality, such as a new rating scale and adaptability to participant movement during the tests, along with other user experience changes.

ACM MM’25 Demo: SDART: Spatial Dart AR Simulation with Hand-Tracked Input

SDART: Spatial Dart AR Simulation with Hand-Tracked Input

ACM Multimedia 2025

October 27 – October 31, 2025

Dublin, Ireland

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Milad Ghanbari (AAU, Austria), Wei Zhou (Cardiff, UK), Cosmin Stejerean (Meta, US), Christian Timmerer (AAU, Austria), Hadi Amirpour (AAU, Austria)

Abstract: We present a physics-driven 3D dart-throwing interaction system for Apple Vision Pro (AVP), developed using Unity 6 engine and running in augmented reality (AR) mode on the device. The system utilizes the PolySpatial and Apple’s ARKit software development kits (SDKs) to ensure hand input and tracking in order to intuitively spawn, grab, and throw virtual darts similar to real darts. The application benefits from physics simulations alongside the innovative no-controller input system of AVP to manipulate objects realistically in an unbounded spatial volume. By implementing spatial distance measurement, scoring logic, and recording user performance, this project enables user studies on quality of experience in interactive experiences. To evaluate the perceived quality and realism of the interaction, we conducted a subjective study with 10 participants using a structured questionnaire. The study measured various aspects of the user experience, including visual and spatial realism, control fidelity, depth perception, immersiveness, and enjoyment. Results indicate high mean opinion scores (MOS) across key dimensions. Link to video: Link

Hadi
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ICCV VQualA’25: VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results

VQualA 2025 Challenge on Image Super-Resolution Generated Content Quality Assessment: Methods and Results

ICCV VQualA 2025

October 19 – October 23, 2025

Hawai’i, USA

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Hadi Amirpour (AAU, Austria), et al.

Abstract: This paper presents the ISRGC-Q Challenge, built upon the Image Super-Resolution Generated Content Quality Assessment (ISRGen-QA) dataset, and organized as part of the Visual Quality Assessment (VQualA) Competition at the ICCV 2025 Workshops. Unlike existing Super-Resolution Image Quality Assessment (SR-IQA) datasets, ISRGen-QA places greater emphasis on SR images generated by the latest generative approaches, including Generative Adversarial Networks (GANs) and diffusion models. The primary goal of this challenge is to analyze the unique artifacts introduced by modern super-resolution techniques and to evaluate their perceptual quality effectively. A total of 108 participants registered for the challenge, with 4 teams submitting valid solutions and fact sheets for the final testing phase. These submissions demonstrated state-of-the-art (SOTA) performance on the ISRGen-QA dataset. The project is publicly available at: https://github.com/Lighting-YXLI/ISRGen-QA.

ICCV VQualA’25: VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

VQualA 2025 Challenge on Face Image Quality Assessment: Methods and Results

ICCV VQualA 2025

October 19 – October 23, 2025

Hawai’i, USA

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MohammadAli Hamidi (University of Cagliari, Italy), Hadi Amirpour (AAU, Austria), et al.

Abstract: Face images have become integral to various applications. but real-world capture conditions often lead to degradations such as noise, blur, compression artifacts, and poor lighting. These degradations negatively impact image quality and downstream tasks. To promote advancements in face image quality assessment (FIQA), we introduce the VQualA 2025 Challenge on Face Image Quality Assessment, part of ICCV 2025 Workshops. Participants developed efficient models (≤0.5 GFLOPs, ≤5M parameters) predicting Mean Opinion Scores (MOS) under realistic degradations. Submissions were rigorously evaluated using objective metrics and human perceptual judgments. The challenge attracted 127 participants, resulting in 1519 valid final submissions. Detailed methodologies and results are presented, contributing to practical FIQA solutions.

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