Test-Time Scaling for Multi-View Diffusion Models: Generation, Verification, and Search

Test-time scaling (TTS) improves generative performance by allocating additional inference computation, yet its effectiveness for structured outputs such as multi-view diffusion models remains underexplored. In this work, we systematically investigate how generation, verification, and search interact when the generated views must remain consistent. We first find that joint multi-view generation exhibits stronger Oracle scaling than independent per-view generation across multiple evaluated models, while revealing a fundamental tension between preserving cross-view consistency through set-level selection and improving individual views through view-level selection. To address this challenge, we introduce DINO-centroid, a lightweight verifier that leverages cross-sample consensus for complete-set selection and uncertain-view identification. Our analysis shows that DINO-centroid becomes increasingly aligned with the ground-truth representation as the sampling budget grows, suggesting that the verifier itself exhibits a form of scaling behavior absent from conventional TTS verifiers. Building on these findings, we propose Exploration–Refinement Search (E-R Search), a strategy that combines set-level exploration with targeted view-level regeneration by formulating the latter as a diffusion inverse problem. Under matched inference budgets, our strategy achieves higher reconstruction quality than existing test-time search baselines. Our findings highlight the importance of accounting for component dependencies in generation, verification, and search for effective TTS of structured outputs.