Hugging Face Trending Papers
Can Learning to Answer Spatial Questions Improve Novel-View Generation?
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arXiv Computer Vision
5d ago
Can Learning to Answer Spatial Questions Improve Novel-View Generation?
arXiv:2610.20610v1 Announce Type: new Abstract: Generating novel views from limited observations requires preserving both the appearance and spatial structure of a scene. Although recent approaches e...
arXiv Machine Learning
Jun 3
Zero-Shot 3D Question Answering via Hierarchical View-to-Token Transportation
arXiv:2606. 03100v1 Announce Type: cross Abstract: Recently, zero-shot 3D scene understanding via 2D Vision-Language Models (VLMs) has gained increasing research interest due to their promising spatial reasoning capabilities.
arXiv Computation and Language
Sep 30
Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering
arXiv:2609.38177v1 Announce Type: cross Abstract: Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs...
arXiv AI
Aug 25
Object-Uni: A Unified Model for Object-Centric Spatial Understanding and Controllable Generation
Object-Uni is a unified model that integrates pose perception, spatial reasoning, pose-conditioned generation, and novel view synthesis for object-centric spatial understanding and controllable image generation. It treats object pose as an explicit geometric variable shared across tasks and introduces a viewpoint-based orientation abstraction to make pose interpretable by multimodal large language models. The authors also create a new benchmark, UniSpatial-80K, and demonstrate that Object‑Uni improves both pose understanding and pose‑controllable generation compared to existing models.
arXiv AI
Jun 26
Semantic Generative Tuning for Unified Multimodal Models
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
arXiv Computer Vision
4d ago
DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models
DK‑VLM introduces Dual‑Kernel Attention, where a visual token’s attention is computed from both its input position and its geometric position derived from 3D points and camera poses. A semantic kernel preserves pretrained attention while a geometric kernel modulates attention via point displacements and relative view transformations, with a learned gate fusing the two. The model also employs Reference‑Frame Composition supervision to update its reference frame across successive judgments, achieving top scores on six spatial benchmarks and strong performance on VSI‑Bench and ReVSI‑32.