arXiv Computer Vision
GeoCo-SAVi: Geometry-Consistent Slot Attention for Explicitly Editable Object Representations
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arXiv Computer Vision
Sep 21
VideoReloc: Long-Term Indoor Video Relocalization against a Kilobyte-Scale Semantic Scene Graph
VideoReloc presents a method for long‑term indoor video relocalization that relies on a compact semantic scene graph rather than visual appearance. By adaptively selecting clip lengths based on odometry and object‑motion criteria, the system gathers spatial evidence, verifies poses through object triplets, and refines orientation using box faces and gravity cues. This approach achieves high localization accuracy with a tiny 100 kB map, outperforming traditional appearance‑based methods on RIO10 and ReplicaCAD datasets.
arXiv Computer Vision
Oct 27
Fovea-JEPA: Overcoming Geometric Blind Spots in V-JEPA for Camera Motion Recognition
arXiv:2610.34033v1 Announce Type: new Abstract: Camera motion recognition requires separating viewpoint changes caused by camera translation, rotation, or zoom from object motion and scene dynamics....
arXiv AI
Jun 12
GeoWorld-VLM: Geometry from World Models for Vision-Language Models
arXiv:2605. 16713v2 Announce Type: replace-cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behind, and between.
arXiv Computer Vision
Nov 2
GeoFlip: Benchmarking Geometry-Sensitive Physics in Video Generation
GeoFlip is a new benchmark for video generation models that tests their ability to produce physically correct outcomes when the initial geometry of a scene is altered. The benchmark presents 482 pairs of scenes with controlled changes in extent, placement, and mechanism configuration, and evaluates models on event correctness, structural integrity, and similarity to reference videos. Across 16 evaluated models, the best event correctness averaged only 27.98%, highlighting significant gaps in geometry‑sensitive physical reasoning.
arXiv Computer Vision
4d ago
GeoFlip Preview: Benchmarking Geometry-Sensitive Physics in Video Generation
arXiv:2610.42002v2 Announce Type: replace Abstract: Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video g...
arXiv AI
Oct 5
OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection
OmniAct3D is a framework that adapts Vision Foundation Model (VFM) detectors trained on perspective images to equirectangular projection (ERP) panoramic images for 3D detection. It introduces the ERP-Ray Geometry Adapter (ERGA-Ray) to model spherical rays, the Visual-Action Reasoning Chain (VARC) to ground hypotheses in panoramic evidence, and the Appearance-Guided Heading Expert (AGHE) to recover high‑resolution heading cues. The method achieves a 2.96 NDS improvement on Spheriverse and a 24.87 mAP gain on PanoMMOcc, while preserving most of the baseline’s performance across configurations.