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Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling — arXiv:2608.29322

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29322
License (as archived): CC0-1.0
Archived: 2026-10-17T09:58:57.864Z
SHA-256 of the archived text: 4b283b3a475baf8aa237c8d2753bb32eb6ba0995fe506c444fafefd00b224296
Stable URL of this page: https://metraipolis.com/documento/dc_7b02ee9ca0ca

How to cite

Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling — arXiv:2608.29322 — arXiv (en), https://arxiv.org/abs/2608.29322 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7b02ee9ca0ca (Metraipolis archive dc_7b02ee9ca0ca, sha256 4b283b3a475baf8aa237c8d2753bb32eb6ba0995fe506c444fafefd00b224296)

BibTeX

@misc{dc7b02ee9ca0ca,
  title = {Test-Time Scaling for Video Diffusion Models via Diagnosis-Guided Candidate Recycling — arXiv:2608.29322},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29322},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7b02ee9ca0ca; sha256 4b283b3a475baf8aa237c8d2753bb32eb6ba0995fe506c444fafefd00b224296},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29322. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent video diffusion models have achieved remarkable generation quality, but high-fidelity results still largely depend on closed-source systems or costly large-scale infrastructure. Test-time scaling (TTS) offers a training-free way to improve lightweight generators by spending additional inference compute, yet existing methods mostly remain within a noise-search paradigm: they sample, select, or perturb denoising trajectories and discard low-scoring candidates after expensive generation. This generate-and-discard process wastes not only computation but also the partial motion, layout, or appearance structure already encoded in recoverable samples. We present \textbf{GEARS} (\textbf{G}uided \textbf{E}diting for \textbf{A}daptive \textbf{R}ecycling \textbf{S}earch), a training-free framework that introduces {diagnosis-guided candidate recycling} into video TTS by turning such candidates into editable priors through a generation-evaluation-editing loop. GEARS consists of two collaborative components. The \textbf{Stage-Aware Scheduler} determines what to repair, when to repair it, and which candidates should be preserved, recycled, or discarded. The \textbf{Candidate Recycler} diagnoses recoverable failures from keyframes and multi-dimensional reward feedback, derives candidate-specific repair prompts, and repairs the corresponding candidates through manifold-aware latent SDEdit. The repaired candidates are recycled into the search pool, creating refinement paths beyond standard noise perturbation while preserving useful structure. Under matched NFE budgets, GEARS consistently outperforms existing video TTS methods on VBench, bringing a 1.3B model to a total score comparable to a 14B counterpart, and ablations verify the necessity of adaptive scheduling, diagnosis-conditioned editing, and manifold-aware re-denoising. Code is available on GitHub.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.29322 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T09:28:48.025Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).