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Scene-Consistent Benchmark

Updated 21 December 2025
  • Scene-Consistent Benchmark is a comprehensive evaluation suite that enforces geometric, semantic, relational, and temporal consistency in complex, multi-modal scenes.
  • It integrates hybrid data sources, explicit scene-to-condition mapping, and detailed complexity profiling to ensure rigorous assessment of scene outputs.
  • The framework employs metrics like PSNR, SSIM, and specialized scene-graph scores to measure multi-view stability, temporal coherence, and relational accuracy.

A Scene-Consistent Benchmark is a rigorously designed evaluation suite, dataset, and set of protocols whose primary objective is to measure, enforce, and facilitate modeling of scene consistency in complex, multi-modal settings. Scene consistency, as formalized in recent research, encompasses geometric, semantic, relational, and temporal coherence within and across generated or interpreted scenes, with applications in image/video generation, simulation, spatial reasoning, urban modeling, and embodied AI. The scene-consistent benchmark paradigm demands that systems generate or interpret multi-object and multi-relational data such that all predictions, across both spatial and temporal axes, respect global scene structure, local and global context, and the underlying physical, semantic, and relational constraints imposed by the scenario.

1. Definitions and Scene Consistency Principles

Scene consistency is defined as the property that all outputs (e.g., images, videos, scene graphs, or multi-modal responses) remain coherent and non-contradictory under variations in viewpoint, time, conditioning, and modality, and that they strictly conform to the physical, geometric, or logical structure of the underlying scene. This criterion can be decomposed as follows (Li et al., 18 Mar 2025, Wang et al., 22 Nov 2025, Lee et al., 16 Oct 2025, Zhou et al., 30 Aug 2024, Chen et al., 23 Nov 2024, Xie et al., 14 Dec 2025):

  • Geometric Consistency: The preservation of spatial arrangements, geometry, and physical attributes under scene transformations or across different modalities (e.g., images and 3D representations).
  • Semantic and Relational Consistency: The maintenance of inter-object relationships and class integrity across output modalities and time (e.g., object A is always to the left of object B, or subject–object relations are temporally sustained).
  • Temporal Consistency: For video or sequential data, the property that scene structure persists without artifacts such as flicker, hallucinated change, or identity swapping when the system is queried sequentially.
  • Cross-View Consistency: The system’s ability to maintain corresponding semantics and geometry when presented with different virtual or real views (perspectives) of the same environment.

A scene-consistent benchmark, then, is one whose design enforces these properties through its data construction, evaluation metrics, and task structure.

2. Design Methodologies and Construction Pipelines

State-of-the-art scene-consistent benchmarks follow tightly controlled data and protocol pipelines to guarantee scene consistency and enable fine-grained diagnostic analysis (Li et al., 18 Mar 2025, Wang et al., 22 Nov 2025, Beche et al., 22 Mar 2025, Xie et al., 14 Dec 2025):

  • Hybrid Data Sources: Blending real-world and high-fidelity simulated or reconstructed scenes (e.g., SimWorld’s 1:1 digital twin of a real-world mine (Li et al., 18 Mar 2025), ClaraVid’s synthesized but artifact-minimized aerial scenes (Beche et al., 22 Mar 2025)).
  • Multi-modal, Multi-view Supervision: Ensuring that for any scene, complete RGB, depth, semantic, panoptic, and/or 3D geometric views are available, with perfectly aligned annotations.
  • Explicit Scene-to-Condition Mapping: Scenes are represented as multi-track signals: segmentation maps, bounding boxes, prompts, and natural-language captions, used as conditions or ground-truth for both training and evaluation.
  • Complexity Profiling and Sampling: Scene or environment difficulty is quantitatively profiled (e.g., via Delentropic Scene Profile in ClaraVid (Beche et al., 22 Mar 2025) or explicit object count/density/occlusion metrics in InfiniBench (Wang et al., 22 Nov 2025)) and stratified to provide balanced coverage across complexity scales.
  • Formal Annotation and Label Consistency: Semantic, temporal, and relational labels are harmonized across scenes and sources to eliminate label bias in evaluation (cf. unified class schema and alignment in SSCBench (Li et al., 2023); relational graph normalization in Scene-Bench (Chen et al., 23 Nov 2024)).

3. Evaluation Metrics, Protocols, and Consistency Measurement

A defining feature of scene-consistent benchmarks is the extensive suite of metrics that assess not only raw accuracy or fidelity, but also various axes of scene consistency (Li et al., 18 Mar 2025, Xie et al., 14 Dec 2025, Wang et al., 22 Nov 2025, Chen et al., 26 Aug 2025, Chen et al., 23 Nov 2024):

Core Metric Categories

Metric Type Formula/Key Expression Assessed Property
Geometric Consistency PSNR, SSIM, LPIPS, DISTS, MEt3R, Chamfer Distance View-to-view stability, 3D alignment
Temporal Consistency DISTS, Warp loss, per-frame/sequence agreement Flicker/smoothness
Relational SGScore, Object/Relation Recall, SoftSPICE, Scene Graphs Object/relationship accuracy
Structural/Logical Cross-view consistency (e.g., C{(t)}_{cross}), Scene Graph matching Global, intermodal logic
Domain Robustness F1_{\mathrm{seen}}, F1_{\mathrm{unseen}}, domain gap Δ Transfer/generalizability
Complexity-aware Performance vs. complexity (e.g., μ from DSP, N, ρ, O) Robustness to "hard" cases

Scene-consistent benchmarks often combine these scores in an evaluation matrix to force models to balance all axes rather than overfit one (e.g., quality, geometric, temporal, relational, and complexity axes in Style4D-Bench (Chen et al., 26 Aug 2025) and SimWorld (Li et al., 18 Mar 2025)).

Protocol Innovations

  • Revisit Trajectories: Assessing scene-consistency by revisiting arbitrary past viewpoints/camera poses and measuring alignment to ground truth at those frames (3DScenePrompt (Lee et al., 16 Oct 2025)).
  • Commutative Metric Evaluation: Enforcing order invariance in scene change detection (e.g., requiring identical predictions for (t₀, t₁) and (t₁, t₀); see GeSCD (Kim et al., 10 Sep 2024)).
  • Scene-Graph Feedback Loops: Iteratively correcting generation via chain-of-thought LLM-based diagnosis and targeted refinements (Scene-Bench (Chen et al., 23 Nov 2024)).
  • Block-level and Modality-level QA Scoring: Decomposing interleaved text-image outputs into scene graphs and systematically querying scene and block-level requirements with VQA modules (ISG-Bench (Chen et al., 26 Nov 2024)).

4. Impact Across Research Domains

Scene-consistent benchmarks have catalyzed advances in multiple research areas by establishing new evaluation standards that penalize failure modes invisible to earlier protocols:

Key empirical findings across studies reveal that:

  • Performance on scene-consistency metrics is often orthogonal to classic generative metrics such as FID or CLIPScore (Chen et al., 23 Nov 2024, Xie et al., 14 Dec 2025).
  • Training with scene-consistent data and pipelines yields up to 25% relative gains on downstream metrics in both detection and segmentation (Li et al., 18 Mar 2025).
  • Measures targeting scene consistency (e.g., new attention-based or graph-based losses) produce outputs strongly preferred in human studies and on newly proposed VLM-based alignment scores (Xie et al., 14 Dec 2025).

5. Limitations, Open Challenges, and Future Prospects

Despite their sophistication, existing scene-consistent benchmarks exhibit areas for improvement (Li et al., 18 Mar 2025, Wang et al., 22 Nov 2025, Xie et al., 14 Dec 2025, Chen et al., 26 Nov 2024, Chen et al., 26 Aug 2025, Beche et al., 22 Mar 2025):

  • Domain and Modality Coverage: Many benchmarks remain focused on narrow domains (e.g., indoor, driving, or aerial scenes); transitions to broader environments require extensible assets, richer label spaces, and multimodal integration.
  • Computation and Scalability: High-fidelity benchmarks may demand intensive simulation, annotation, or optimization (e.g., SimWorld XL, Style4D), limiting feasibility at larger scales or in resource-constrained research.
  • Temporal and Multimodal Consistency: Video-centric or interleaved tasks remain challenging, as flicker, identity drift, or modality-specific inconsistency are not always captured by existing scores (see temporal ablation studies, (Chen et al., 26 Aug 2025)).
  • Automated Diagnosis and Feedback: While steps such as scene-graph feedback loops and interleaved QA are promising, robust automation of error correction and interpretable benchmarking across arbitrary modalities remains incomplete.
  • Measurement of Complexity Impact: The explicit use of scene complexity priors (e.g., delentropy in ClaraVid, (Beche et al., 22 Mar 2025)) to guide dataset curation, performance interpretation, and curriculum learning is newly emerging.
  • Failure Mode Exposure: Benchmarks such as ISG-Bench reveal that even as holistic scores improve, block- and image-level inconsistencies persist in current generation systems, especially for open-ended or visuo-linguistically entangled tasks.

Recommendations for future benchmarks include broadening scene types, integrating richer 3D and temporal annotation, leveraging new automated geometric/semantic scoring backbones (e.g., Pers. Geometry, dynamic SLAM), and developing more interpretable, multi-level feedback mechanisms.

6. Representative Benchmarks and Comparative Features

The table below organizes key scene-consistent benchmarks described in the literature and their principal evaluation axes:

Benchmark Domain(s) Consistency Axes Representative Metrics
SimWorld (Li et al., 18 Mar 2025) Driving (real/virtual) Geometric, semantic, label, domain FID, pixel diversity, mAP, mIoU
GeSCD (Kim et al., 10 Sep 2024) Change detection, VPR Temporal, cross-domain, commutativity F1 (bidirectional), TC, domain gap
InfiniBench (Wang et al., 22 Nov 2025) 3D spatial reasoning Object, relational, occlusion Prompt fidelity, realism, CLIP, coverage
Style4D-Bench (Chen et al., 26 Aug 2025) Dynamic 3D stylization Spatio-temporal, multi-view, subject DISTS, LPIPS, Warp loss, DINO
Scene-Bench (Chen et al., 23 Nov 2024) Graph→Image generation Factual (object/relationship) SGScore, object/rel recall, feedback
ClaraVid (Beche et al., 22 Mar 2025) Aerial holistic rec. Multi-view, modality, complexity-aware PSNR, SSIM, DSP cor., mIoU, AbsRel
ISG-Bench (Chen et al., 26 Nov 2024) Interleaved text-image Block, image, structural, holistic Manual QA (struct, block, image, hol.)
EWMBench (Yue et al., 14 May 2025) Embodied world models Scene, motion, semantic, diversity SceneC, HSD, nDTW, BLEU, CLIP, Logic
RoadSceneBench (Liu et al., 27 Nov 2025) Road structural reasoning Frame, temporal, topology, attribute Precision, Recall, Consistency, HRRP-T

These systems have become reference points for evaluating and advancing scene consistency in the emerging generation of multi-modal AI systems.

7. References

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