arXiv (All)
open access
Video Generation Models: A Survey of Post-Training and Alignment
Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics. Despite strong generative priors learned through large-scale pretraining, pretrained video models often fail to reliably follow human intent, mainta
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
- MotionSpec: Spectral Trajectory Supervision for Motion-Consistent Video Generation#1064639
- Temporal State Transport in Video Generation: Diagnosing and Correcting Spectral Imbalance#985854
- StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding#620720
- StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding#619041
- PhyProbe: Rethinking Physical Consistency Evaluation in Generated Videos#1142978
Record · ID 1155747
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