Frozen Scenes, Shifting Winners: Configuration Fragility in Text-to-3D Evaluation

arXiv:2610.00447 · cs.AI, cs.CL, cs.CV, cs.GR, cs.MM · Submitted 2026-09-30 · Read on arXiv

cs.AI, cs.CL, cs.CV, cs.GR, cs.MM

Submitted: 2026-09-30

Updated: 2026-09-30

Terminology

• Alignment — 30× in this paper · explained in 1 episode
• Benchmark — 15× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 8× in this paper
• In International Conference on Learning Representations — 5× in this paper
• Provenance — 5× in this paper · explained in 4 episodes
• CVF International Conference on Computer Vision — 4× in this paper
• 3D-DefectBench — 3× in this paper · explained in 1 episode
• Generative AI — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Embodied AI — 2× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 2× in this paper
• In International Conference on Machine Learning — 2× in this paper
• Robustness — 2× in this paper · explained in 5 episodes
• ACM International Conference on Software Engineering — 1× in this paper
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• A Cross-Model VLM-Judge Protocol for Single-Image 3D Mesh Quality (and Why Cheap Proxies Fall Short)
• HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing
• Eval Factsheets: A Structured Framework for Documenting AI Evaluations
• Evaluation Cards: An Interpretive Layer for AI Evaluation Reporting
• T$^3$Bench: Benchmarking Current Progress in Text-to-3D Generation
• TDBench: A Benchmark for Top-Down Image Understanding with Reliability Analysis of Vision-Language Models
• SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis
• EVA-CLIP: Improved Training Techniques for CLIP at Scale
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• 3DGen-Bench: Comprehensive Benchmark Suite for 3D Generative Models
• 3D-DefectBench: A Controlled Factorial Study of Vision-Language Model Evaluation Pipelines for Fine-Grained 3D Generation Defects

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