The paper introduces Doppio, a new video dataset of falling ground coffee with precise per‑frame weight measurements, aimed at enabling contactless mass estimation. It evaluates several deep learning models—spatial feed‑forward and recurrent spatio‑temporal networks—to predict the cumulative weight of falling particles. The results show that these vision‑based approaches can accurately estimate weight, suggesting a practical foundation for future contactless measurement systems.
PACT is an end‑to‑end model that jointly learns human pose, contacts, and contact forces from monocular video. It augments a human reconstruction foundation model with learnable contact‑force tokens and a temporal transformer, and uses physics‑based supervision to enforce consistency between motion and forces. The authors also create a data annotation pipeline and a real‑world climbing benchmark, ForceWall, to train and evaluate the system, achieving state‑of‑the‑art performance and better generalization than staged approaches.
By Rikhat Akizhanov (MBZUAI), Yangsong Zhang (MBZUAI), Nikolai Kaliazin (MBZUAI), Peter Wolf (ETH Z\"urich), Yoshihiko Nakamura (MBZUAI), Pascal Fua (EPFL), Fabio Pizzati (MBZUAI), Ivan Laptev (MBZUAI)
arXiv:2604. 22160v2 Announce Type: replace-cross Abstract: Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation.
By Eric Li, Arijit Dasgupta, Yoni Friedman, Mathieu Huot, Vikash Mansinghka, Thomas O'Connell, William T. Freeman, Joshua B. Tenenbaum
Event cameras, also known as neuromorphic cameras, have gained significant attention in recent years due to their high temporal resolution, high dynamic range, and low power consumption. While many studies and datasets in neuromorphic vision have focused on automotive and drone applications, human-centric daily-life scenarios remain largely underrepresented, despite their importance for developing and benchmarking event-based perception systems.
arXiv:2607. 12909v1 Announce Type: cross Abstract: Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system.
By Wenjun Xia, Zhicheng Peng, Haopeng Li, Zhengdi Zhang
arXiv:2606. 18732v1 Announce Type: new Abstract: This work presents the development of hybrid models that integrate spiking neural networks (SNNs) with components of convolutional neural networks (CNNs) to learn from simulated event-based camera data (Dynamic Vision Sensor, DVS) generated from conventional smartphone videos.
By Guillermo Rojas, Gonzalo Soto, Daniel Yunge
arXiv:2510. 26369v2 Announce Type: replace Abstract: Logistics warehouses have struggled with labor shortages, but the inbound processes remain particularly human-powered.
By Kazuma Kano, Yuki Mori, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi
arXiv:2610.13242v1 Announce Type: new
Abstract: Currently, the animal production sector is undergoing rapid development of techniques that allow automatic monitoring of animal behaviour and health. T...
By Sebastian Puchala, Dominik Gronkiewicz, Marcin Go{\l}\k{e}biewski, Grzegorz Grodkowski, Grzegorz Gwardys
Falls are a major health concern for older adults, and wearable sensors have been widely explored for detecting falls and enabling timely intervention. However, real-world falls are extremely rare: collecting 100 of them requires an estimated 100,000 days of monitoring, resulting in severely limited labelled data for training machine learning models.
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...
By Arkya Jyoti Bagchi, Ritul Jangir, Varun Raskar
SnapPhysics is a training‑free framework that reconstructs 3D objects and estimates their physical properties—mass, friction, and center of gravity—from a single image. It combines instance‑level 3D reconstruction with a physics‑aware scene graph to provide geometric grounding and inter‑object relationships for vision‑language model reasoning. Experiments on 3D‑FRONT and real captured scenes show significant improvements over existing methods, enabling physically interactive mixed reality experiences without manual tuning.
By Suji Kang, Seok-Young Kim, Young Bin Kim, Taewook Ha, Dieter Schmalstieg, Shohei Mori, Woontack Woo
arXiv:2608.31025v1 Announce Type: new
Abstract: Inferring object dynamics from visual observations is essential for intelligent agents to reason about and interact with the physical world, yet remain...
By Jailing Lin, Jikuan Zhang, Jianhua Sun