arXiv (All)
open access
Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation
Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Training the generator on its own rollouts exposes it to these imperfect histories. However, existing video-level distribution matching distillation (DMD)
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
- ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation#1076579
- Enhancing Autoregressive Video Generation via Representation Adversarial Distillation#1145204
- Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation#1122345
- From Scores to Samples: Elastic Forcing for Autoregressive Video Generation#1144872
- PDMD: Projected Distribution Matching Distillation for Video Diffusion Models#1130413
Record · ID 1142230
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