arXiv (All)
open access
InstantViR: Real-Time Video Inverse Problem Solver with Distilled Diffusion Prior
Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constraints. Diffusion-based priors currently deliver state-of-the-art reconstructions, but existing approaches either adapt image diffusion models with ad hoc
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FastVR: Efficient Streaming Video Restoration with One-Step Diffusion#1131958
- Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models#1141544
- LoCoVSR: Local Context Diffusion Posterior Sampling for Video Super-Resolution#1117073
- VDOT++: Unified Few-Step Video Generation via Unbalanced Optimal Transport Distillation#1187899
- Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection#1070663
Record · ID 1342360
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive