GeoWorld-VLM: Geometry from World Models for Vision-Language Models

• URL: http://arxiv.org/abs/2605.16713v1
• Date: Fri, 15 May 2026 23:52:11 GMT
• Title: GeoWorld-VLM: Geometry from World Models for Vision-Language Models
• Abstract summary: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations.<n>We introduce GeoWorld-VLM, a VLM-side distillation framework that transfers geometric structure from frozen video world models intoVLMs.<n>GeoWorld-VLM fine-tunes only the image encoder and multimodal projector, aligning post-projector image features with intermediate world-model representations while leaving the main backbone frozen.
• Score: 10.86505613923278
• License: http://creativecommons.org/licenses/by/4.0/
• 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. One cause of this failure arises before language reasoning begins: the visual pathway may compress or discard critical 3D structural cues during feature extraction, so the language model receives image representations that are already insufficient for reliable spatial judgment. We introduce GeoWorld-VLM, a VLM-side distillation framework that transfers geometric structure from frozen camera-conditioned video world models into VLMs. GeoWorld-VLM fine-tunes only the image encoder and multimodal projector, aligning post-projector image features with intermediate world-model representations while leaving the main backbone frozen. Given images, a prompt, and a sampled camera trajectory, the world-model teacher converts static visual input into a synthetic multi-view spatial signal. Training combines spatial answer supervision, teacher-student feature alignment, and a preservation anchor to the original VLM. Since the language model remains frozen, GeoWorld-VLM preserves the original model's linguistic capabilities while attributing spatial improvements to the enhanced visual pathway. To evaluate the effectiveness and generality of the proposed method, we apply GeoWorld-VLM to two distinct VLM architectures and observe consistent improvements across both backbones. GeoWorld-VLM improves performance by approximately 4 percent on both the What'sUp and VSR benchmarks, suggesting that world-model-guided visual alignment generalizes across model structures and spatial reasoning datasets.

Related papers

• Training-Free Open-Vocabulary Visual Grounding for Remote Sensing Images and Videos [52.516842108549135]
  Remote sensing visual grounding aims to localize a referred target in a remote sensing image or video according to a natural language expression.<n>Existing RSVG methods usually rely on task-specific manual annotations, which are costly to collect and inevitably limited in covering the diversity of real-world geospatial scenarios.<n>We propose RSVGZeroOV, a training-free framework that leverages frozen generic foundation models for zero-shot open-vocabulary RSVG.
  arXiv  Detail & Related papers  (2026-06-15T02:31:32Z)
• Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models [31.851717131965824]
  We present the first systematic frozen-feature probing study of Vision-Language Models (VLMs) and Video Generation Models (VGMs)<n>Using the lightweight probe, our framework enables a controlled comparison of what information is already encoded in frozen representations from two model families.<n>Results reveal a clear complementarity: VLMs are stronger at semantic tagging and instance grouping, while VGMs provide more accessible signals for dense geometry and camera motion.
  arXiv  Detail & Related papers  (2026-05-27T08:20:04Z)
• World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning [18.658825757342544]
  Vision-language models (VLMs) have shown strong performance on static visual understanding.<n>They still struggle with dynamic spatial reasoning that requires imagining how scenes evolve under egocentric motion.<n>We propose World2VLM, a training framework that distills spatial imagination from a generative world model into a vision-language model.
  arXiv  Detail & Related papers  (2026-04-29T17:48:01Z)
• Loc3R-VLM: Language-based Localization and 3D Reasoning with Vision-Language Models [47.045362895601556]
  Loc3R-VLM is a framework that equips 2D Vision-Language Models with advanced 3D understanding capabilities from monocular video input.<n>Inspired by human spatial cognition, Loc3R-VLM relies on two joint objectives: global layout reconstruction and explicit situation modeling.<n>To ensure geometric consistency and metric-scale alignment, we leverage lightweight camera pose priors extracted from a pre-trained 3D foundation model.
  arXiv  Detail & Related papers  (2026-03-18T17:59:10Z)
• TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation [70.23578202012048]
  Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch.<n>We propose TagaVLM (Topology-Aware Global Action reasoning), an end-to-end framework that explicitly injects topological structures into the VLM backbone.<n>To enhance topological node information, an Interleaved Navigation Prompt strengthens node-level visual-text alignment.<n>With the embedded topological graph, the model is capable of global action reasoning, allowing for robust path correction.
  arXiv  Detail & Related papers  (2026-03-03T13:28:07Z)
• RAYNOVA: Scale-Temporal Autoregressive World Modeling in Ray Space [51.441415833480505]
  RAYNOVA is a multiview world model for driving scenarios that employs a dual-causal autoregressive framework.<n>It constructs an isotropic-temporal representation across views, frames, and scales based on relative Plcker-ray positional encoding.
  arXiv  Detail & Related papers  (2026-02-24T08:41:40Z)
• Seeing through Imagination: Learning Scene Geometry via Implicit Spatial World Modeling [68.14113731953971]
  This paper introduces MILO, an Implicit spatIaL wOrld modeling paradigm that simulates human-like imagination.<n>We show that our approach significantly enhances spatial reasoning capabilities across multiple baselines and benchmarks.
  arXiv  Detail & Related papers  (2025-12-01T16:01:41Z)
• Evo-0: Vision-Language-Action Model with Implicit Spatial Understanding [11.222744122842023]
  We introduce a plug-and-play module that implicitly incorporates 3D geometry features into Vision-Language-Action (VLA) models.<n>Our method significantly improves the performance of state-of-the-art VLA models across diverse scenarios.
  arXiv  Detail & Related papers  (2025-07-01T04:05:47Z)
• REVISION: Rendering Tools Enable Spatial Fidelity in Vision-Language Models [67.55362046790512]
  Vision-language models lack the ability to correctly reason over spatial relationships. We develop the REVISION framework which improves spatial fidelity in vision-language models. Our results and findings indicate that utilizing rendering-based frameworks is an effective approach for developing spatially-aware models.
  arXiv  Detail & Related papers  (2024-08-05T04:51:46Z)
• AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
  We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
  arXiv  Detail & Related papers  (2023-07-07T17:59:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.