Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection

• URL: http://arxiv.org/abs/2603.26745v1
• Date: Mon, 23 Mar 2026 08:45:44 GMT
• Title: Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection
• Abstract summary: Video anomaly detection is a critical task in embodied multimedia systems for intelligent surveillance and forensic analysis.<n>We propose Motion Semantics Guided Normalizing Flow (MSG-Flow) that decomposes skeleton-based VAD into hierarchical motion semantics modeling.<n>MSG-Flow achieves state-of-the-art performance with 88.1% and 75.8% AUC respectively.
• Score: 21.81092485652255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As embodied perception systems increasingly bridge digital and physical realms in interactive multimedia applications, the need for privacy-preserving approaches to understand human activities in physical environments has become paramount. Video anomaly detection is a critical task in such embodied multimedia systems for intelligent surveillance and forensic analysis. Skeleton-based approaches have emerged as a privacy-preserving alternative that processes physical world information through abstract human pose representations while discarding sensitive visual attributes such as identity and facial features. However, existing skeleton-based methods predominantly model continuous motion trajectories in a monolithic manner, failing to capture the hierarchical nature of human activities composed of discrete semantic primitives and fine-grained kinematic details, which leads to reduced discriminability when anomalies manifest at different abstraction levels. In this regard, we propose Motion Semantics Guided Normalizing Flow (MSG-Flow) that decomposes skeleton-based VAD into hierarchical motion semantics modeling. It employs vector quantized variational auto-encoder to discretize continuous motion into interpretable primitives, an autoregressive Transformer to model semantic-level temporal dependencies, and a conditional normalizing flow to capture detail-level pose variations. Extensive experiments on benchmarks (HR-ShanghaiTech & HR-UBnormal) demonstrate that MSG-Flow achieves state-of-the-art performance with 88.1% and 75.8% AUC respectively.

Related papers

• DisPOSE: Projected Polystochastic Diffusion for Self-Supervised Multi-View 3D Human Pose Estimation [58.47973015036709]
  DisPOSE is a self-supervised framework that approximates the inherently discrete multi-view person-assignment problem.<n>By employing differentiable Sinkhorn projections during denoising, our model learns to guide solutions toward valid and feasible assignments.<n>The proposed approach outperforms current state-of-the-art self-supervised methods on standard datasets.
  arXiv  Detail & Related papers  (2026-06-05T16:14:39Z)
• RefineVAD: Semantic-Guided Feature Recalibration for Weakly Supervised Video Anomaly Detection [2.770730728142587]
  Weakly-Supervised Video Anomaly Detection aims to identify anomalous events using only video-level labels.<n>Existing methods often oversimplify the anomaly space by treating all abnormal events as a single category.<n>We propose RefineVAD, a novel framework that mimics this dual-process reasoning.
  arXiv  Detail & Related papers  (2025-11-17T10:15:34Z)
• Dynamic Avatar-Scene Rendering from Human-centric Context [75.95641456716373]
  We propose bf Separate-then-Map (StM) strategy to bridge separately defined and optimized models.<n>StM significantly outperforms existing state-of-the-art methods in both visual quality and rendering accuracy.
  arXiv  Detail & Related papers  (2025-11-13T17:39:06Z)
• Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection [39.65895515365808]
  We propose a novel zero-shot video anomaly detection framework, unlocking the potential of skeleton data via action typicality and uniqueness learning.<n>Our method achieves state-of-the-art results against skeleton-based methods on four large-scale VAD datasets.
  arXiv  Detail & Related papers  (2025-09-14T02:51:32Z)
• Implicit Neural Representation for Physics-driven Actuated Soft Bodies [15.261578025057593]
  This paper utilizes a differentiable, quasi-static, and physics-based simulation layer to optimize for actuation signals parameterized by neural networks. We define a function that enables a continuous mapping from a spatial point in the material space to the actuation value. We extend our implicit model to mandible kinematics for the particular case of facial animation and show that we can reliably reproduce facial expressions captured with high-quality capture systems.
  arXiv  Detail & Related papers  (2024-01-26T13:42:12Z)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion [70.33381660741861]
  We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions. We show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity.
  arXiv  Detail & Related papers  (2023-09-04T05:43:48Z)
• Priority-Centric Human Motion Generation in Discrete Latent Space [59.401128190423535]
  We introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM) for text-to-motion generation. M2DM incorporates a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token.
  arXiv  Detail & Related papers  (2023-08-28T10:40:16Z)
• MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras [98.40768911788854]
  We introduce MoCo-Flow, a representation that models the dynamic scene using a 4D continuous time-variant function. At the heart of our work lies a novel optimization formulation, which is constrained by a motion consensus regularization on the motion flow. We extensively evaluate MoCo-Flow on several datasets that contain human motions of varying complexity.
  arXiv  Detail & Related papers  (2021-06-08T16:03:50Z)
• Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild [31.40575057347465]
  We tackle the task of video-based visual emotion recognition in the wild. Standard methodologies that rely solely on the extraction of bodily and facial features often fall short of accurate emotion prediction. We aspire to alleviate this problem by leveraging visual context in the form of scene characteristics and attributes.
  arXiv  Detail & Related papers  (2021-05-16T17:31:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.