arXiv AI By Youngrae Kim, Yejin Jang, Eunho Kim, Youjin Sung, Kun-Woo Song, Sangpil Kim, Sang Ho Yoon

HAPACT: A Benchmark For Human-Centric Physical Impact Localization in Movies

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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.

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arXiv Computer Vision
Aug 27

Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining

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.

By Koshiro Nagano, Fumiaki Sato, Ryo Hachiuma, Kazuki Tsutsukawa, Taiki Sekii
arXiv AI
Jun 29

EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

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.

By Chengjun Yu, Xuhan Zhu, Chaoqun Du, Pengfei Yu, Wei Zhai, Yang Cao, Zheng-Jun Zha