The paper introduces HAPACT, a new benchmark for human‑centric physical impact localization in movies. It defines a task that requires identifying when and where abrupt impact events occur on the human body within a video. The dataset contains over 2.8K video‑query pairs and more than 20K finely annotated frames, and the authors evaluate existing methods, showing that the task remains challenging and motivating further research.
arXiv:2602.05718v2 Announce Type: replace Abstract: Point-supervised Temporal Action Localization (PTAL) adopts a lightly frame-annotated paradigm (\textit{i.e.}, labeling only a single frame per act...
arXiv:2606. 12047v1 Announce Type: cross Abstract: In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language.
The paper introduces Skeleton-Language feature Pooling Switching, a weakly‑supervised vision‑language pretraining strategy for skeleton‑based zero‑shot spatio‑temporal action localization. It replaces video‑level pooling with instance‑level feature computation during inference, enabling the model to estimate unseen actions without costly annotations. Additionally, Scene‑Mixed Discriminative Contrastive Learning is proposed to separate actions at the instance level within mixed scenes using a MIL framework, and experiments on four public datasets confirm the method’s effectiveness.
arXiv:2608. 07932v2 Announce Type: replace Abstract: Sports video analysis is crucial for athletic analytics and broadcasting enhancement.
arXiv:2607. 13421v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to retrieve the visual trajectory of a specific object from a video stream as described by a natural language expression.
arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.
In this paper, we address the problem of zero-shot understanding of accidents from surveillance videos by identifying when an impact event occurs, what type of impact it is, and where in the frame it occurs using natural language. We propose a three-stage pipeline that decomposes the accident understanding into when, what, and where.
arXiv:2608.29759v1 Announce Type: cross Abstract: We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view C...
The paper introduces query‑guided grounding methods for video understanding, covering object‑level, relationship‑level, and moment‑level tasks. For object grounding, QDETRv and TOViT enable one‑shot and few‑shot localization of unseen objects using query‑context fusion, memory modules, and language‑aligned vision encoders. Relationship grounding is addressed with a few‑shot referring framework that optimizes a T‑partite random field, while moment grounding is tackled by the Moment Alignment Transformer (MATR), which employs dynamic time warping and self‑supervised pretraining for video‑to‑video and text‑to‑video retrieval.
SOMA is a new benchmark that tests fine‑grained egocentric body‑motion understanding, featuring 1,632 human‑verified questions across body configuration, motion dynamics, and temporal reasoning. The study evaluates 37 multimodal large language models (MLLMs) and finds that humans achieve 68.5% accuracy while the best MLLM (Qwen3.6‑27B) reaches only 50.0% zero‑shot accuracy. The authors also provide a training dataset and a multimodal baseline to help close the performance gap and highlight common failure modes such as misreading body parts and temporal errors.
arXiv:2610.19526v1 Announce Type: new Abstract: Temporal action detection in long untrimmed videos suffers from inaccurate localization and low-quality proposals, especially with sparse annotations a...
SituHOI is a large‑scale RGB‑D dataset that captures full‑body human‑object interactions in 16 indoor rooms, comprising 18,429 annotated clips and about 86 hours of activity. The dataset pairs moving‑camera recordings with static scene scans, object geometry, language descriptions, and estimated human and object trajectories, all registered in a shared floor‑aligned frame via a pipeline that integrates camera localization, depth‑based alignment, human mesh recovery, and object tracking. The data enable the FlexHOI‑Bench benchmark for goal‑conditioned joint motion prediction, and a reference predictor, SharedHOI, demonstrates strong performance on world‑space accuracy and human‑object coordination. whyItMatters":"The dataset provides scalable, scene‑aligned supervision for modeling human‑object interaction, addressing a key limitation of existing datasets and enabling more accurate motion prediction benchmarks."