How We Built an AI‑Powered Automated Video Editing Pipeline for Short‑Form Marketing

01 Background

In today’s marketing landscape short videos are the primary user‑touchpoint, but manual production (shooting, selecting, editing) of a 30‑second clip can take hours, making large‑scale campaigns costly and inefficient.

02 Overall Architecture

We designed an "automated editing pipeline" that treats video creation like a factory line: Material Layer → Content Understanding → Content Generation → Multi‑Agent orchestration → Final AIGC solution . The pipeline consists of three logical layers (raw material, understanding, generation) and four core stages:

Material ingestion & AI visual analysis

AI‑directed script & storyboard creation

Automated editing & rendering (audio‑video alignment, BGM recommendation, TTS)

Multi‑channel distribution & product library management

03 Core Technical Modules

Module 1 – Multimodal Content Understanding

Goal: Convert unstructured video into searchable tags and structured metadata. We use a proprietary video‑understanding model (or open‑source alternatives such as Qwen2.5‑VL‑72B‑Instruct) to perform shot‑level segmentation, filter low‑quality or privacy‑sensitive clips, and generate visual and audio tags (object detection, scene classification, OCR, ASR, emotion analysis). The output is a JSON record containing captions, categories, tags, and quality metrics.

{
  "caption": {
    "title": "自制巧克力香蕉棒",
    "description": "视频展示了一种自制的巧克力香蕉棒...",
    "categroy": {"content_category": {"general": "生活娱乐", "detail": "生活娱乐-美食探店"}, "scene_category": {"general": "室内场景", "detail": "室内场景-家居环境"}},
    "tags": {"tags": ["美食","甜品","自制"], "emotion": ["快乐","满足"]},
    "video_content": {"text_content": "试试看 真的有梦龙的感觉！", "object_content": ["香蕉","巧克力","竹签"]},
    "video_quality": {"definition": "高等", "color": "彩色", "is_complete": "是", "quality": "高"},
    "other": {"is_pornography": "否", "is_politics": "否"}
  }
}

We also build an intelligent tag taxonomy (visual tags, audio tags) and a quality‑assessment model (blur detection, aesthetic scoring).

Module 2 – AI‑Driven Script & Storyboard Engine

Using the structured output from Module 1, a large language model (LLM) generates a marketing‑focused script and shot list. The pipeline includes:

Atomic script structure definition

Target audience, pain points, cognitive hooks, emotional anchors

Content rhythm (golden three seconds, narrative model, key scenes, memorable “golden sentences”, climax)

We fine‑tune a Qwen‑3 series COT model with reinforcement learning, where the reward function incorporates predicted view‑count, likes, comments, and share metrics. This closes the loop: the model proposes a script, the system evaluates it, and the RL agent iterates to maximize the reward.

Module 3 – High‑Performance Automated Rendering Service

Goal: Stable, efficient synthesis of video, effects, and output. Key techniques:

Shot generation using DeepSeek‑R1‑0528 to specify lens type, style, and motion.

Self‑developed video quality detector (F1‑Score 0.85) to filter low‑quality results.

Post‑processing chain aligns AI‑generated visuals with TTS audio, inserts meme clips, and performs subtitle segmentation.

Sample JSON shot description:

{
  "分镜片段": "毕业旅行到底该怎么玩？这条白沙湖公路直接封神！",
  "类型": "视频",
  "视频描述": "汽车行驶在雪山湖泊公路，前方有白沙湖观景台指示牌",
  "表情包描述": ""
}

04 Results & Metrics

Two real‑world campaigns (graduation travel and a cultural “flash‑60‑year” theme) generated tens of thousands of views, dozens of new followers, and thousands of likes. Compared with manual production, AI‑generated videos achieved:

Play‑count increase: 40×

Like increase: 8×

Cost reduction per video: 70 %

Production time cut from ~205 min to ~14 min (≈ 15× faster)

05 Lessons Learned

Early versions omitted OCR, causing loss of on‑screen text; over‑reliance on script‑first generation led to mismatched audio‑visual timing. Integrating a bidirectional pipeline (script ↔ shot) and adding robust quality checks mitigated these issues.

06 Future Directions

Planned improvements include integrating larger‑scale foundation models, enhancing consistency between narration and visuals, adding AR/VR support for live‑stream content, and expanding the SaaS offering to verticals such as education, healthcare, and legal services.