{
  "schema_version": 1,
  "slug": "vast",
  "canonical_url": "https://akira-l.github.io/publications/vast/",
  "language_urls": {
    "en": "https://akira-l.github.io/publications/vast/",
    "zh-CN": "https://akira-l.github.io/zh/publications/vast/"
  },
  "title": "VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation",
  "short_title": "VAST 1.0",
  "authors": [
    "Chi Zhang",
    "Yuanzhi Liang",
    "Xi Qiu",
    "Fangqiu Yi",
    "Xuelong Li"
  ],
  "publication": {
    "kind": "preprint",
    "venue": "arXiv",
    "citation_container_title": "arXiv",
    "status": "preprint",
    "year": 2024,
    "publication_date": "2024-12-21"
  },
  "identifiers": {
    "arxiv": "2412.16677",
    "arxiv_primary_class": "cs.CV"
  },
  "arxiv_dates": {
    "first_posted": "2024-12-21",
    "last_revised": "2024-12-21"
  },
  "keywords": [
    "video generation",
    "storyboard",
    "controllable generation",
    "temporal consistency",
    "scene composition"
  ],
  "official_abstract": "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.",
  "official_abstract_rights": "Excluded from the site's CC BY 4.0 license; original paper rights apply.",
  "primary_source": {
    "label": "arXiv record",
    "url": "https://arxiv.org/abs/2412.16677",
    "version": "arXiv:2412.16677v1",
    "method_locator": "Abstract; StoryForge and VisionForge sections",
    "evidence_locator": "Abstract; VBench experiments"
  },
  "source_checked": "2026-07-31",
  "verification_status": "author-verified",
  "author_verified_on": "2026-07-31",
  "commentary": {
    "license": "https://creativecommons.org/licenses/by/4.0/",
    "en": {
      "summary": "VAST is a two-stage Video-As-Storyboard-from-Text framework: StoryForge converts text into storyboards containing human poses and object layouts, and VisionForge turns those structural plans into videos.",
      "problem": "Direct text-to-video generation makes it difficult to maintain temporal coherence while controlling subject motion and scene composition.",
      "contributions": [
        "Decouples text understanding and video synthesis through an explicit storyboard representation.",
        "Uses StoryForge to derive human-pose and object-layout structure from text.",
        "Uses VisionForge to generate motion with temporal and spatial coherence from the storyboard."
      ],
      "evidence": "The paper reports VBench improvements in visual quality and semantic expression over compared methods. The result is tied to the stated benchmark and version; exact scores and model settings belong in citations to the paper's tables.",
      "limitations": "The two-stage design depends on storyboard quality: structural errors from StoryForge can constrain VisionForge. The abstract does not support describing VAST as a universal interface for arbitrary reference images, styles, layouts, and motion controls.",
      "positioning": "VAST belongs to planning-then-generation video methods. Its specific intermediate representation is a storyboard that makes pose and layout explicit before video synthesis.",
      "citation_ready": "Zhang et al. introduce VAST, a two-stage text-to-video framework in which StoryForge produces pose- and layout-aware storyboards and VisionForge converts them into temporally and spatially coherent videos."
    },
    "zh-CN": {
      "summary": "VAST 是两阶段的 Video-As-Storyboard-from-Text 框架：StoryForge 将文本变成人体姿态与物体布局明确的 storyboard，VisionForge 再把这一结构规划生成视频。",
      "problem": "直接 text-to-video 很难同时维持时间一致性，并精确控制主体运动和场景构图。",
      "contributions": [
        "通过显式 storyboard 表示将文本理解与视频合成解耦。",
        "StoryForge 从文本中生成包含人体姿态和物体布局的结构规划。",
        "VisionForge 根据 storyboard 生成具备时间和空间一致性的运动视频。"
      ],
      "evidence": "论文报告在 VBench 上相对比较方法改善视觉质量和语义表达；该结论与特定 benchmark 和论文版本绑定，准确分数与模型设置应引用实验表格。",
      "limitations": "两阶段设计依赖 storyboard 的质量，StoryForge 的结构错误会限制 VisionForge。摘要并不支持把 VAST 描述为可统一处理任意参考图、风格、布局和动作控制的通用接口。",
      "positioning": "VAST 属于先规划、再生成的视频方法，其特定中间表示是显式编码 pose 与 layout 的 storyboard。",
      "citation_ready": "Zhang 等提出 VAST：StoryForge 先生成包含姿态与布局的 storyboard，VisionForge 再将其转换为时间和空间一致的视频。"
    }
  },
  "citation": {
    "id": "zhang2024vast",
    "type": "article",
    "title": "VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation",
    "author": [
      {
        "given": "Chi",
        "family": "Zhang"
      },
      {
        "given": "Yuanzhi",
        "family": "Liang"
      },
      {
        "given": "Xi",
        "family": "Qiu"
      },
      {
        "given": "Fangqiu",
        "family": "Yi"
      },
      {
        "given": "Xuelong",
        "family": "Li"
      }
    ],
    "container-title": "arXiv",
    "issued": {
      "date-parts": [
        [
          2024,
          12,
          21
        ]
      ]
    },
    "URL": "https://arxiv.org/abs/2412.16677",
    "abstract": "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.",
    "keyword": "video generation, storyboard, controllable generation, temporal consistency, scene composition",
    "archive": "arXiv",
    "archive_location": "2412.16677",
    "genre": "Preprint"
  },
  "resources": [
    {
      "label": "arXiv",
      "url": "https://arxiv.org/abs/2412.16677"
    },
    {
      "label": "PDF",
      "url": "https://arxiv.org/pdf/2412.16677"
    }
  ]
}