MindView: Benchmarking Generative Multi-View Spatial Intelligence

Yanbo Wang; Siqi Luo; Siyu Zhang; Chenming Zhu; Wenzhao Zheng; Qian Yu; Juncheng Yan; Bin Fu

cs.CVOct 20, 2026

MindView: Benchmarking Generative Multi-View Spatial Intelligence

Authors: Yanbo Wang, Siqi Luo, Siyu Zhang, Chenming Zhu, Wenzhao Zheng, Qian Yu, Juncheng Yan, Bin Fu,

Organizations: Tsinghua University · Shanghai Artificial Intelligence Laboratory · Shanghai Jiao Tong University · Fudan University ·

Abstract

Spatial intelligence has recently been assessed through generative tasks such as spatially grounded image editing, yet language-guided generation of spatially consistent views from partial multi-view observations remains underexplored. We introduce MindView, a benchmark for generative multi-view spatial intelligence with 1,500 samples from 513 scenes across three indoor datasets. Its seven tasks form two families: geometry-dominant tasks specify changes in camera position and orientation, while semantics-dominant tasks require inferring target viewpoints by grounding referred objects and interpreting spatial relations and reference-frame cues. Evaluation of eleven generators reveals substantial room for improvement on MindView. A paired context analysis further shows that all eleven generators obtain lower overall scores under the expanded context setting. These findings motivate us to investigate whether task-specific supervision can improve target-view generation and whether models can use additional multi-view evidence effectively. We therefore fine-tune BAGEL on the MindView training dataset, improving its overall score by 24.3 points and yielding gains across all seven MindView tasks. Beyond these generation gains, the fine-tuned model also yields higher scores on three spatial-understanding benchmarks without training on their annotations, suggesting that learning to generate spatially grounded views may benefit spatial understanding. To improve evidence utilization under expanded context, we propose MindCue, a training-free spatial evidence selector guided by a target-view hypothesis. MindCue reverses the drop in overall score under expanded context and outperforms the standard-context baseline. Overall, MindView provides a concrete testbed for generative multi-view spatial intelligence.

Figures & tables

Figure 1: Overview of MindView, showing seven task examples, benchmark statistics, six metrics, and representative results. Red A marks anchor views, and targets are real images.

Figure 2: Five-stage offline construction pipeline for MindView.

Figure 3: Distribution of the 1,500 MindView samples across source datasets and tasks.

Model	DINO-I	VLM Judge			Geometry		Overall
Model	DINO-I	Semantic	Spatial	Quality	RScore	TScore	Overall
Open-Source Models
BAGEL	48.3	44.5	33.6	47.1	34.7	31.9	40.0
FLUX.2	52.2	49.5	39.4	80.4	35.5	38.6	49.3
Qwen-Image-Edit-2511 $\dagger$	52.4	49.2	38.6	69.5	37.2	40.3	47.9
Emu3.5 $\dagger$	44.9	41.0	31.2	67.2	36.0	49.9	45.0

Table 1: Quantitative comparison on MindView. Task-macro scores over 1,500 samples. and mark best and second-best across displayed models, respectively;

\dagger

denotes tiled context views.

Figure 4: Qualitative comparison on Composition and Multi-Cue Viewpoint Inference.

Model	Geometry-Dominant Tasks			Semantics-Dominant Tasks
Model	Trans.	Rot.	Comp.	ROG	TFR	OCR	MCVI
Open-Source Models
BAGEL	41.7	38.8	36.1	43.2	38.7	44.1	37.6
FLUX.2	53.0	47.0	44.1	50.5	49.9	53.9	46.6
Qwen-Image-Edit-2511 $\dagger$	53.4	45.9	41.6	49.9	46.6	57.0	40.7
Emu3.5 $\dagger$	55.7	43.6	40.7	42.5	45.0	52.3	35.5

Table 2: Task-wise comparison on MindView. Per-task Overall scores across all displayed models. Highlighting and

\dagger

follow Table 1 ; abbreviations are defined in Section 3.1 .

Figure 5: Overall score trajectories across nested context settings.

Figure 6: Overview of the MindCue framework.

Model	Fixed $C_{6}$ (6.0)	Fixed $C_{10}$ (10.0)	CounterSel (8.6)	RandDrop (7.4)	MindCue (7.4)	$\Delta C_{6}$	$\Delta C_{10}$
BAGEL	40.0	39.0	37.1	39.6	41.1	+1.1	+2.1
BAGEL-SFT	64.4	63.2	62.6	64.1	65.1	+0.8	+1.9

Table 4: Task-macro Overall under context selection. Parentheses give mean view counts; and mark the best and second-best policies per model, respectively.

\Delta C_{k}

is MindCue’s score-point gain over Fixed

C_{k}

, computed before rounding.

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 7: Translation example from ARKitScenes.

Figure 8: Rotation example from ScanNet.

Figure 9: Composition example from ARKitScenes.

Figure 10: Relational Object Grounding example from ScanNet++.

Figure 11: Target-Facing Reorientation example from ScanNet.

Figure 12: Object-Centric Repositioning example from ScanNet.

Figure 13: Multi-Cue Viewpoint Inference example from ScanNet++.

Model	Semantic		Spatial		Quality
Model	Human	VLM	Human	VLM	Human	VLM
GPT-Image-2	68.4	63.4	63.3	50.2	76.3	84.8
Nano Banana Pro	62.6	59.5	59.5	47.4	72.5	79.8
SenseNova-U1.5	57.6	54.1	51.5	43.5	66.8	70.3
BAGEL	45.5	45.4	43.0	33.4	44.1	48.6

Appendix

Table 5: Human and VLM-judge component scores on the shared 210-sample subset. Human scores are three-expert averages; all values are on a 0–100 scale, and higher is better.

Task	$N$	Real target (GT)			Contexts
Task	$N$	$\Delta R$ ( ∘ )	$\Delta C$ (cm)	$\Delta D$ (%)	$\Delta R$ ( ∘ )	$\Delta L$ (cm)	$\Delta D$ (%)
Translation	30	0.85	4.96	1.09	1.09	6.02	1.39
Rotation	30	0.52	1.92	0.58	0.65	2.14	0.79
Composition	30	0.96	4.60	0.95	0.88	4.71	1.18
ROG	30	0.82	3.25	0.99	0.93	2.62	0.97
TFR	30	0.63	1.60	0.55	0.70	1.88	0.80

Appendix

Table 6: VGGT reference stability across generators. Only the generation changes; ten contexts and the real target stay fixed. Entries are sample medians after averaging six generator pairs per sample.

\Delta R

: relative-rotation change;

\Delta C

: aligned GT-center displacement;

\Delta L

: context-pair distance change;

\Delta D

: symmetric relative depth change. Distances are depth-calibrated; lower values indicate greater stability.

Figure 14: VGGT evaluation of GPT-Image-2 and BAGEL outputs. For the ScanNet TFR query “Turn to face the telephone,” the top row shows six generation contexts ( A : anchor); evaluation uses ten fixed references, the real target, and one generation. Reconstructions are independently depth-calibrated and rigidly aligned using only the ten reference camera centers, without scale fitting. All cameras are VGGT estimates; GT denotes the real target image. Dark red marks points contributed by BAGEL’s generation; all other scene points are gray.

Task	Target-view hypothesis
Geometry-Dominant Tasks
Trans.	Move from the reference position along its horizontal camera axes by the requested displacements, keeping its orientation and intrinsics.
Rot.	Keep the reference position and intrinsics. Rotate about world-up by the requested yaw, with left positive and right negative. A turn-around is $180^{\circ}$ .
Comp.	Apply translations and rotations in query order from the reference pose. Each displacement uses the current horizontal camera axes, accounting for preceding rotations. Keep the reference intrinsics.
Semantics-Dominant Tasks
ROG	For each core-supported target centroid, place cameras at the requested distance along rays toward input-camera centers. Face the centroid and inherit source intrinsics; select the highest-scoring object–camera pair.

Appendix

Table 7: Task-specific target-view hypotheses in MindCue.

Configuration	Mean views	Mean (s)
BAGEL, $C_{6}$	6	38.528
BAGEL, $C_{10}$	10	50.625
BAGEL, selected views	7.30	42.476
MindCue + BAGEL, same views	7.30	43.913
MindCue component		Mean (s)
VGGT reconstruction		0.572

Appendix

Table 8: Runtime on a single A100 80GB. View counts refer to generator inputs, with identical images and order in the selected-view rows. Means are weighted and rounded independently.

Model	Trans.						Rot.
Model	DINO-I	Sem.	Spa.	Qual.	RScore	TScore	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	47.7	44.2	34.5	48.6	47.8	27.5	45.8	41.4	30.4	46.4	33.7	35.1
FLUX.2	53.2	49.8	40.9	85.3	59.0	29.7	46.7	42.4	33.9	78.5	31.9	48.7
Qwen-Image-Edit-2511 $\dagger$	57.3	51.8	41.0	67.3	67.8	35.4	43.1	36.9	29.3	69.8	32.2	63.8
Emu3.5 $\dagger$	52.0	46.0	38.0	69.6	80.1	48.4	38.7	32.0	24.5	63.3	30.3	72.5
DreamOmni2	56.3	51.3	41.8	61.8	74.2	41.1	39.8	31.8	25.5	60.8	23.7	63.7

Appendix

Table 9: Component scores on MindView for Translation and Rotation.

Model	Comp.						ROG
Model	DINO-I	Sem.	Spa.	Qual.	RScore	TScore	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	42.7	34.9	28.0	45.7	29.8	35.5	50.9	48.8	36.2	45.2	42.5	35.4
FLUX.2	43.6	40.0	33.5	80.8	32.9	33.6	54.1	52.0	40.5	81.3	33.2	42.2
Qwen-Image-Edit-2511 $\dagger$	43.7	38.3	30.7	66.5	31.6	39.0	55.2	54.6	42.0	69.3	38.4	39.8
Emu3.5 $\dagger$	38.0	31.3	26.9	63.8	26.8	57.3	49.0	45.5	32.8	58.5	29.9	39.1
DreamOmni2	36.0	28.5	24.4	64.0	19.5	42.6	35.4	29.4	23.6	70.5	11.2	32.3

Appendix

Table 10: Component scores on MindView for Composition and Relational Object Grounding.

Model	TFR						OCR
Model	DINO-I	Sem.	Spa.	Qual.	RScore	TScore	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	43.4	42.0	30.9	45.0	32.4	38.3	61.7	59.2	44.1	50.8	27.0	22.0
FLUX.2	50.5	48.5	38.3	76.1	32.7	53.4	70.0	68.4	52.9	77.6	28.4	25.8
Qwen-Image-Edit-2511 $\dagger$	48.8	45.6	35.3	75.6	31.0	43.5	74.2	79.4	60.6	61.9	31.1	34.8
Emu3.5 $\dagger$	40.6	40.8	29.8	73.3	25.7	59.4	62.7	61.2	43.8	68.5	37.7	40.0
DreamOmni2	31.0	25.6	19.2	67.7	13.2	53.4	62.7	60.2	45.6	63.4	23.8	31.5

Appendix

Table 11: Component scores on MindView for Target-Facing Reorientation and Object-Centric Repositioning.

Model	MCVI
Model	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	45.8	41.2	31.4	47.9	30.0	29.3
FLUX.2	47.5	45.8	36.0	82.9	30.4	36.7
Qwen-Image-Edit-2511 $\dagger$	44.6	37.9	31.4	76.2	28.8	25.5
Emu3.5 $\dagger$	32.9	30.6	22.6	73.0	21.4	32.3
DreamOmni2	31.0	24.9	21.2	70.1	11.3	34.5

Appendix

Table 12: Component scores on MindView for Multi-Cue Viewpoint Inference.

Model	$C_{3}$						$C_{10}$
Model	DINO-I	Sem.	Spa.	Qual.	RScore	TScore	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	46.7	43.2	32.4	47.3	33.0	34.0	45.9	40.8	31.0	48.5	32.4	35.5
FLUX.2	49.6	46.4	37.7	85.9	33.7	41.5	51.5	48.7	38.1	69.3	32.5	38.0
Qwen-Image-Edit-2511 $\dagger$	50.2	47.1	37.2	69.8	33.8	38.1	51.6	48.1	37.9	71.1	36.2	38.3
Emu3.5 $\dagger$	44.5	38.9	28.6	59.5	34.8	46.8	44.9	39.9	30.6	68.3	33.3	49.1
DreamOmni2	42.7	37.6	30.8	76.9	25.1	36.1	32.2	27.7	22.1	59.0	25.9	51.8

Appendix

Table 13: Task-macro component scores under reduced and expanded contexts.

Model	Policy	DINO-I	Sem.	Spa.	Qual.	RScore	TScore
BAGEL	Fixed $C_{6}$	48.3	44.5	33.6	47.1	34.7	31.9
	Fixed $C_{10}$	45.9	40.8	31.0	48.5	32.4	35.5
	CounterSel	43.7	38.4	29.3	48.4	29.5	33.0
	RandDrop	47.3	42.8	32.0	47.9	34.0	33.3
	MindCue	49.2	44.9	33.9	47.6	36.6	34.7
BAGEL-SFT	Fixed $C_{6}$	68.0	71.0	57.1	61.8	61.7	66.5

Appendix

Table 14: Task-macro component scores for the five context policies in Table 4 .

Figure 15: Qualitative comparison across all seven MindView tasks. Each row shows a query summary, six input contexts, the held-out real target (GT), and outputs from the selected generators named above the columns. A red border and A identify the anchor when one is required.

Spatial Reasoning Benchmarks

Spatial reasoning benchmarks test whether AI systems can perceive, represent, and act on geometric, relational, and viewpoint-dependent information.

See all 162 papers

Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence
May 11, 2026Yanbing Zhang, Bo Wang, Jianhui Liu +9Visual Spatial Reasoning VLM Reasoning
Joy Future Academy
Exploring Spatial Intelligence from a Generative Perspective
Apr 22, 2026Muzhi Zhu, Shunyao Jiang, Huanyi Zheng +9Spatial Reasoning Unified Multimodal Models
Zhejiang University, State Key Laboratory of CAD & CG · Ant Group · Zhejiang University of Technology +1
3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models
Mar 8, 2026Shaoxiong Zhan, Yanlin Lai, Zheng Liu +6Visual Spatial Reasoning Spatial Reasoning in Language Models
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Pengcheng Laboratory, Shenzhen, China · School of Software Engineering, Chongqing University, Chongqing, China

May 11, 2026J/K move · Enter open · S save

Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence

Yanbing Zhang, Bo Wang, Jianhui Liu, Nan Jiang+8

Joy Future Academy

Visual Spatial Reasoning VLM Reasoning

Open paper AskPDF arXiv

Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static observation. We propose Thinking with Novel Views (TwNV), a paradigm that integrates generative novel-view synthesis into the reasoning loop: a Reasoner LMM identifies spatial ambiguity, instructs a Painter to synthesize an alternative viewpoint, and re-examines the scene with the additional evidence. Through systematic experiments we address three research questions. (1) Instruction format: numerical camera-pose specifications yield more reliable view control than free-form language. (2) Generation fidelity: synthesized view quality is tightly coupled with downstream spatial accuracy. (3) Inference-time visual scaling: iterative multi-turn view refinement further improves performance, echoing recent scaling trends in language reasoning. Across four spatial subtask categories and four LMM architectures (both closed- and open-source), TwNV consistently improves accuracy by +1.3 to +3.9 pp, with the largest gains on viewpoint-sensitive subtasks. These results establish novel-view generation as a practical lever for advancing spatial intelligence of LMMs.

Abstract

Spatial Reasoning Benchmarks

Explore similar work

Thinking with Novel Views: A Systematic Analysis of Generative-Augmented Spatial Intelligence

Exploring Spatial Intelligence from a Generative Perspective

3ViewSense: Spatial and Mental Perspective Reasoning from Orthographic Views in Vision-Language Models