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.
Moving object segmentation is conventionally formulated at the sequence and object level, marking an entire instance as moving once it moves anywhere in a video. Recent instantaneous formulations dist...
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.
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...
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.
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...
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...
arXiv:2604. 22160v2 Announce Type: replace-cross Abstract: Human visual perception offers valuable insights for understanding computational principles of motion-based scene interpretation.
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...
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.
Dual-State Slot Attention (DSSA) is a self‑supervised framework that separates per‑frame appearance from temporally stable identity within each slot, addressing conflicts that cause slot swapping in video object‑centric learning. It introduces a learned recurrent transition for identity updates and competition‑modulated aggregation to suppress weakly matching slots, thereby improving segmentation quality and temporal consistency. Experiments on MOVi‑C, MOVi‑D, and YouTube‑VIS show DSSA outperforms prior methods and enhances downstream object recognition and video dynamics prediction.
The paper introduces FaVOS, a benchmark for Video Object Segmentation (VOS) that focuses on scenarios where target objects appear only intermittently over long videos. It demonstrates that the standard J&F metric can be gamed by empty predictions, allowing trivial models to outperform strong ones like SAM 3. To address this, the authors propose Volumetric J&F, which treats mask sequences as spatio‑temporal volumes, reducing the influence of target‑absence rewards while maintaining sensitivity to segmentation quality and temporal structure.
The paper introduces RGBTR‑Motion, a new benchmark that synchronizes RGB, thermal, and radar data with dense moving‑instance masks and consistent identities for surveillance scenes. It also presents SAM‑Radar, a segmentation and tracking framework that fuses calibrated RGBT features with radar returns, using radar‑aware detection and motion supervision to reject clutter and maintain identity continuity during low visibility or occlusion. SAM‑Radar achieves state‑of‑the‑art performance, improving IoU, F1‑50, MOTA, HOTA, and IDF1 metrics over existing methods.