DramaChain Bench: An End-to-End Benchmark for Short-Drama Generation

• URL: http://arxiv.org/abs/2609.00646v1
• Date: Tue, 01 Sep 2026 03:23:45 GMT
• Title: DramaChain Bench: An End-to-End Benchmark for Short-Drama Generation
• Abstract summary: DramaChain Bench is the first short-drama benchmark that evaluates every stage of the complete production chain.<n>It is built upon three in-house systems sharing one dimension system, DramaChain Dimensions.
• Score: 20.53004165750949
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Commercial short-drama production follows a multi-stage chain: script, storyboard, keyframe imagery, shot-level video, and the finished short drama. Most existing benchmarks evaluate solely the video-generation stage using pre-authored inputs instead of real upstream pipeline outputs. This leaves two critical questions unanswerable: whether each stage adheres to the original script intent (rather than only its immediate input prompt), and whether disparate shots remain coherent after assembly into multi-episode releases. We present DramaChain Bench, the first short-drama benchmark that evaluates every stage of the complete production chain. It is built upon three in-house systems sharing one dimension system, DramaChain Dimensions: five evaluation axes instantiated at every stage, resolving into 63 leaf dimensions. DramaChain Agent is calibrated against commercial short-drama platforms in both workflow and finished short-drama quality, enabling stage-wise fair comparison across models. DramaChain Labeling System has each of the 5,785 items scored independently by three professional annotators, with all defects spatio-temporally localised and selected from a predefined defect list. This process produces 17,488 valid scores and 255,925 traceable attribution records. The human annotations confirm that upstream defects cascade across the pipeline, demonstrating that final episode quality is not governed by video generation alone. DramaChain Agentic Judge then scores every leaf dimension automatically, gathering evidence over multiple agentic rounds before judging against a per-item checklist; it reproduces the model ranking at a mean PLCC of 0.918, enough to admit new models at no annotation cost.

Related papers

• DramaDirector: Geometry-Guided Short Drama Generation [51.430988173490384]
  We study plot-to-short-drama generation, where a global plot and local context are transformed into visually grounded multi-shot videos.<n>We propose DramaDirector, a geometry-grounded framework that lets the planner borrow cinematographic geometry from a gallery of real short-drama shots.<n>We also introduce DramaBoard, a benchmark built from 35 live-action dramas, 2.8K episodes, and 81K shots.
  arXiv  Detail & Related papers  (2026-06-23T03:50:28Z)
• Crayotter: Traceable Multi-Agent Workflows for Long-Form Video Editing [18.640980775340637]
  Crayotter is an open-source multimodal multi-agent system for prompt-driven video editing.<n>Crayotter organizes production into three phases: coverage-aware material preparation, artifact-based editing research, and tool-grounded timeline execution.
  arXiv  Detail & Related papers  (2026-05-31T14:07:57Z)
• ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation [69.45329019089041]
  Multi-Shot Video Extrapolation (MSVE) is a task that extends an observed frame or clip into a sequence of cinematically structured shots.<n>MSVE reveals that long-video failure is not merely a limitation of context length, but a failure of context allocation.<n>We propose Recursive Context Allocation (ReCA), an inference-time framework that allocates context hierarchically across planning and generation.
  arXiv  Detail & Related papers  (2026-05-26T04:16:56Z)
• One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems [17.754369892506542]
  One Sentence, One Drama is a hierarchical multi-agent framework that transforms a user's single-sentence idea into a fully produced short drama.<n>Our approach is built upon three key components: (1) a multi-agent debate-based story generation module that enforces short-drama pacing and narrative coherence; (2) a 3D-grounded first-frame generation mechanism that establishes a shared spatial reference for consistent character positioning and scene layout across clips; and (3) multi-stage reviewer loops that perform comprehensive error detection and targeted revision across script, visual, and video generation stages.
  arXiv  Detail & Related papers  (2026-05-21T08:15:46Z)
• Soap2Soap: Long Cinematic Video Remaking via Multi-Agent Collaboration [61.98029663481308]
  Soap2Soap is a multi-agent framework that enforces long-term visual consistency through a DualBridge mechanism.<n>A closed-loop verification agent audits identity, stability, and alignment to trigger selective regeneration.
  arXiv  Detail & Related papers  (2026-05-17T12:38:21Z)
• Agentic Video Generation: From Text to Executable Event Graphs via Tool-Constrained LLM Planning [4.964902130083661]
  Existing multi-agent video generation systems use LLM agents to orchestrate neural video generators.<n>We present an agentic system that inverts this paradigm, constructing a formal Graph of Events in Space and Time.<n>A programmatic state backend enforces all simulator constraints through validated tool calls, guaranteeing that every generated specification is executable.
  arXiv  Detail & Related papers  (2026-04-11T23:51:13Z)
• DramaBench: A Six-Dimensional Evaluation Framework for Drama Script Continuation [3.496854427764583]
  Drama script continuation requires models to maintain character consistency, advance plot coherently, and preserve dramatic structurecapabilities.<n>We present DramaBench, the first large-scale benchmark for evaluating drama script continuation across six independent dimensions.
  arXiv  Detail & Related papers  (2025-12-22T04:03:01Z)
• Lights, Camera, Consistency: A Multistage Pipeline for Character-Stable AI Video Stories [5.022547031373416]
  We introduce a method that approaches video generation in a filmmaker-like manner.<n>Instead of creating a video in one step, our proposed pipeline first uses a large language model to generate a detailed production script.<n>This script guides a text-to-image model in creating consistent visuals for each character, which then serve as anchors for a video generation model to synthesize each scene individually.
  arXiv  Detail & Related papers  (2025-12-17T18:10:27Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [70.61101071902596]
  Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines.<n>We introduce VideoGen-of-Thought (VGoT), a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT generates multi-shot videos that outperform state-of-the-art baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2025-03-19T11:59:14Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [76.3175166538482]
  VideoGen-of-Thought (VGoT) is a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT addresses three core challenges: Narrative fragmentation, visual inconsistency, and transition artifacts.<n>Combined in a training-free pipeline, VGoT surpasses strong baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2024-12-03T08:33:50Z)
• Perception Test: A Diagnostic Benchmark for Multimodal Video Models [78.64546291816117]
  We propose a novel multimodal video benchmark to evaluate the perception and reasoning skills of pre-trained multimodal models. The Perception Test focuses on skills (Memory, Abstraction, Physics, Semantics) and types of reasoning (descriptive, explanatory, predictive, counterfactual) across video, audio, and text modalities. The benchmark probes pre-trained models for their transfer capabilities, in a zero-shot / few-shot or limited finetuning regime.
  arXiv  Detail & Related papers  (2023-05-23T07:54:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.