# VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation

arXiv: https://arxiv.org/abs/2412.16677 · HTML: https://mlsanity.com/paper/2412.16677/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2024.

## Built on by (most cited first, 9 of 9)

- [Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation](https://mlsanity.com/paper/2508.03334/) (2025)
- [Seeing What Matters: Visual Preference Policy Optimization for Visual Generation](https://mlsanity.com/paper/2511.18719/) (2025)
- [Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation](https://mlsanity.com/paper/2509.26555/) (2025)
- [ViStoryBench: Comprehensive Benchmark Suite for Story Visualization](https://mlsanity.com/paper/2505.24862/) (2025)
- [Conditional Video Generation for High-Efficiency Video Compression](https://mlsanity.com/paper/2507.15269/) (2025)
- [Learning What to Trust: Bayesian Prior-Guided Optimization for Visual Generation](https://mlsanity.com/paper/2511.18919/) (2025)
- [Reward-Aware Trajectory Shaping for Few-step Visual Generation](https://mlsanity.com/paper/2604.14910/) (2026)
- [Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation](https://mlsanity.com/paper/2604.19234/) (2026)
- [MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models](https://mlsanity.com/paper/2510.17519/) (2025)

## Abstract (arXiv)

Generating high-quality videos from textual descriptions poses challenges in maintaining temporal coherence and control over subject motion. We propose VAST (Video As Storyboard from Text), a two-stage framework to address these challenges and enable high-quality video generation. In the first stage, StoryForge transforms textual descriptions into detailed storyboards, capturing human poses and object layouts to represent the structural essence of the scene. In the second stage, VisionForge generates videos from these storyboards, producing high-quality videos with smooth motion, temporal consistency, and spatial coherence. By decoupling text understanding from video generation, VAST enables precise control over subject dynamics and scene composition. Experiments on the VBench benchmark demonstrate that VAST outperforms existing methods in both visual quality and semantic expression, setting a new standard for dynamic and coherent video generation.