The paper introduces MoRSe, a method that segments only the regions that actually move between two frames, rather than labeling entire objects as moving. Unlike prior approaches that rely on instance-level proposals and often misattribute motion to whole objects, MoRSe jointly interprets two frames with a single RGB encoder, eliminating the need for optical flow or separate mask generators. The authors also release MoRSe-5K, a benchmark of 42 sequences designed to test the model’s ability to differentiate moving and stationary regions under various camera and scene motion conditions, and demonstrate that MoRSe outperforms existing moving object segmentation and dense 4D reconstruction methods with fewer false positives.
By Wenhua Jin, Tiancheng Chen, Sheng Tang, Jiahao Li, Yupeng Liu, Weiqi Zhang, Juntong Fang, Zesong Li
The paper introduces a promptable localized motion representation that generates persistent embeddings for user-specified regions in a video, without cropping or masking the input. By conditioning motion encoding directly on spatial masks while processing the full video, the method produces temporally consistent, region-addressable embeddings that capture local dynamics while preserving global context. These embeddings enable object-level motion transfer for dynamic scene composition and improve localized action classification in multi-actor videos, outperforming global representations that rely on cropping or post-hoc masking.
By Frank Fundel, Malek Ben Alaya, Thomas Ressler-Antal, Stefan Andreas Baumann, Bj\"orn Ommer
arXiv:2609.14466v1 Announce Type: new
Abstract: Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the obse...
By Calvin Galagain, Martyna Poreba, Fran\c{c}ois Goulette
arXiv:2609.36875v1 Announce Type: new
Abstract: Accurate instance segmentation in dynamic scenes is important for downstream applications such as robotics and autonomous driving. Existing Segment Any...
By Jingdong Zhang, Xin Li, Jan Kautz, Wenping Wang, Chris Choy
SAM‑V is a geometry‑aware extension of the Segment Anything Model (SAM) that integrates 3D priors from a feed‑forward geometry model (VGGT) into 2D segmentation. It uses a prompt‑fusion mechanism to combine sparse SAM prompts with view‑specific camera tokens and local VGGT features, enabling a mask decoder that attends to both dense 2D and 3D cues. The resulting end‑to‑end system produces consistent multi‑view instance segmentation in a single forward pass, achieving significant gains on the IGGT 3D tracking benchmark without offline mask matching or explicit 3D reconstruction.
By Jiangshan Gong, Yuqun Wu, Qiqian Fu, Yao Xiao, Chuhang Zou, Shenlong Wang, Derek Hoiem
The paper introduces a biologically inspired framework that learns object‑centric visual representations from raw videos without human annotations or camera calibration. By using motion boundaries detected via optical flow and clustering to create pseudo‑instance masks, the method supervises a single‑image encoder with pixel‑level pairwise metric learning. Training on 195 million pseudo‑labeled frames and expanding to 421 million frames through Motion‑Verified Self‑Training, the approach yields Swin‑based encoders that outperform or match supervised and self‑supervised baselines on tasks such as monocular depth estimation, 3D object detection, 3D occupancy prediction, and end‑to‑end planning.
By Boshi Li, Xiaohui Wang, Xiaoyang Wu, Zhichao Li, Ya Yang, Naiyan Wang
arXiv:2609.39785v1 Announce Type: new
Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised...
By Weijian Jian, Xiaoyue Zhang, Bin Xiao, Chunyu Xie, Yixiao He, Yutao Liu, Dawei Leng, Yuhui Yin
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
arXiv:2508.01661v2 Announce Type: replace
Abstract: Amodal segmentation aims to recover complete object shapes, including occluded regions, serving as an essential technique for user-centric multimed...
By Zhixuan Li, Yujia Liu, Chen Hui, Chenyue Song, Weisi Lin
arXiv:2606. 29531v1 Announce Type: cross Abstract: We propose MotionAtlas, a system for detailed captioning of motion-centric videos, comprising (1) a dedicated human-annotated benchmark, (2) a scalable, high-quality pipeline to construct training samples, and (3) a family of powerful Video-MLLMs.
By Weisong Liu, Haochen Wang, Kuan Gao, Yuhao Wang, Yikang Zhou, Zhongwei Ren, Jacky Mai, Anna Wang, Yanwei Li, Jason Li, Zhaoxiang Zhang
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding.
arXiv:2609.36940v1 Announce Type: new
Abstract: Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are esse...
By Thai Duy Nguyen, Haitian Zhang, Addison Lin Wang