VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2024.
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Citing papers per year: 2025: 7; 2026: 2.
9 papers.
- 2025Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generationcited by 19
- 2025Seeing What Matters: Visual Preference Policy Optimization for Visual Generationcited by 9
- 2025Conditional Video Generation for High-Efficiency Video Compressioncited by 5
- 2025Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generationcited by 5
- 2025Learning What to Trust: Bayesian Prior-Guided Optimization for Visual Generationcited by 4
- 2025ViStoryBench: Comprehensive Benchmark Suite for Story Visualizationcited by 2
- 2026Reward-Aware Trajectory Shaping for Few-step Visual Generationcited by 1
- 2026Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generationcited by 1
- 2025MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Modelscited by 0
- 2026Learning to Credit the Right Steps: Objective-aware Process Optimization for Visual Generation21 Apr 2026
- 2026Reward-Aware Trajectory Shaping for Few-step Visual Generation16 Apr 2026
- 2025Learning What to Trust: Bayesian Prior-Guided Optimization for Visual Generation24 Nov 2025
- 2025Seeing What Matters: Visual Preference Policy Optimization for Visual Generation24 Nov 2025
- 2025MUG-V 10B: High-efficiency Training Pipeline for Large Video Generation Models20 Oct 2025
- 2025Stable Cinemetrics : Structured Taxonomy and Evaluation for Professional Video Generation30 Sep 2025
- 2025Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation5 Aug 2025
- 2025Conditional Video Generation for High-Efficiency Video Compression21 Jul 2025
- 2025ViStoryBench: Comprehensive Benchmark Suite for Story Visualization30 May 2025