SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
Updated 1 h ago · first seen 11 Sept 2026
paper_01M294WYDPBZCXJ1FMAK30Z44W
- Published
- 7 Sept 2026
- T2 · 7 h ago
- arXiv
- 2609.07064
- T2 · 7 h ago
Abstract
Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images -- referred to as spatial intelligence -- remains limited. Existing approaches attempt to address this gap by using real-scene spatial question answering datasets that require dense geometric annotations. However, constructing such labels is costly, time-consuming, and often noisy due to reliance on external perception modules. In this work, we propose a novel paradigm inspired by human cognitive development: learning foundational spatial skills through structured block-manipulation tasks. We introduce SpatialBlock-15k, a synthetic dataset of 15,000 block-stacking problems covering 3D-to-2D projection, viewpoint transformation, and structural combination. The dataset further incorporates controlled color modulation as visual cues to encourage anchor-based reasoning in visually complex conditions. Experiments demonstrate that LVLMs trained on our dataset through either direct answering or reasoning-based prediction significantly outperform baselines and generalize to real-world spatial tasks, despite the dataset's synthetic and compact nature. Code and data are available at https://github.com/rsoohyun/SpatialBlock.
Authors 3
Soohyun Ryu, Sohee Kim, Eunho Yang
Specification
- Official page
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- arXiv id
- 2609.07064
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- Github repo
- rsoohyun/SpatialBlock
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- Hf paper url
- https://huggingface.co/papers/2609.07064
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- Github stars
- 2
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
- Hf comments
- 2
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
- Upvotes
- 68
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 1 h agomedium
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
- Published
- 7 Sept 2026
Source:Hugging Face Hub (public pages, model cards, papers)T2observed 7 h agomedium
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Attributed facts
11
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T211
Freshest observation
1 h ago
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Claim history
Official pageofficial_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/abs/2609.07064 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Abstractabstract1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D structure of the scene depicted in 2D images -- referred to as spatial intelligence -- remains limited. Existing approaches attempt to address this gap by using real-scene spatial question answering datasets that require dense geometric annotations. However, constructing such labels is costly, time-consuming, and often noisy due to reliance on external perception modules. In this work, we propose a novel paradigm inspired by human cognitive development: learning foundational spatial skills through structured block-manipulation tasks. We introduce SpatialBlock-15k, a synthetic dataset of 15,000 block-stacking problems covering 3D-to-2D projection, viewpoint transformation, and structural combination. The dataset further incorporates controlled color modulation as visual cues to encourage anchor-based reasoning in visually complex conditions. Experiments demonstrate that LVLMs trained on our dataset through either direct answering or reasoning-based prediction significantly outperform baselines and generalize to real-world spatial tasks, despite the dataset's synthetic and compact nature. Code and data are available at https://github.com/rsoohyun/SpatialBlock. | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
arXiv idarxiv_id1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 2609.07064 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Github repogithub_repo1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| rsoohyun/SpatialBlock | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Hf paper urlhf_paper_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://huggingface.co/papers/2609.07064 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Github starsmetric.github_stars2
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 2 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
| 0 | → | superseded | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Hf commentsmetric.hf_comments2
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 2 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
| 1 | → | superseded | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Upvotesmetric.upvotes3
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 68 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
| 65 | → | superseded | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
| 61 | → | superseded | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
PDFpdf_url1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/pdf/2609.07064 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Publishedpublished_at1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| 7 Sept 2026 | → current | current | Hugging Face Hub (public pages, model cards, papers)T2 | medium | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
- New paperPaperSpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
New paper: SpatialBlock: Enhancing Spatial Intelligence in LVLMs via Synthetic Block-Stacking Problem
huggingface
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| Hugging Face Hub (public pages, model cards, papers) | huggingface.co/papers | listing | T2· Quality secondary | 1 h ago | 6 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.