REVIEW 18 cited by
StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration
read the original abstract
The advent of AI-Generated Content (AIGC) has spurred research into automated video generation to streamline conventional processes. However, automating storytelling video production, particularly for customized narratives, remains challenging due to the complexity of maintaining subject consistency across shots. While existing approaches like Mora and AesopAgent integrate multiple agents for Story-to-Video (S2V) generation, they fall short in preserving protagonist consistency and supporting Customized Storytelling Video Generation (CSVG). To address these limitations, we propose StoryAgent, a multi-agent framework designed for CSVG. StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process. Notably, our framework includes agents for story design, storyboard generation, video creation, agent coordination, and result evaluation. Leveraging the strengths of different models, StoryAgent enhances control over the generation process, significantly improving character consistency. Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency, while a novel storyboard generation pipeline is proposed to maintain subject consistency across shots. Extensive experiments demonstrate the effectiveness of our approach in synthesizing highly consistent storytelling videos, outperforming state-of-the-art methods. Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.
Forward citations
Cited by 18 Pith papers
-
KathaTrace: Diagnosing Semantic Trajectory Collapse in Generated Visual Narratives
Introduces KathaTrace protocol and KathaBench-25K benchmark to quantify Semantic Trajectory Gap (STG) as the loss of transition meaning in visualized narratives, reporting STG of 23.5 +/- 1.3 across generators.
-
GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling
GroundShot introduces entity-grounded shot scheduling with online visual memory to improve consistency in multi-shot video generation and presents GroundBench for entity-level evaluation.
-
EM-Vid: Training-Free Entity-Centric Memory for Efficient and Consistent Multi-Shot Video Generation
EM-Vid introduces an entity-centric latent patch memory bank with sparse token conditioning and budgeted updates for training-free consistent multi-shot video generation.
-
CausalCine: Real-Time Autoregressive Generation for Multi-Shot Video Narratives
CausalCine enables real-time causal autoregressive multi-shot video generation via multi-shot training, content-aware memory routing for coherence, and distillation to few-step inference.
-
Agentic Video Generation: From Text to Executable Event Graphs via Tool-Constrained LLM Planning
An LLM agentic system builds executable GEST specifications via a hierarchical Director-Scene Builder architecture with Relation Subagents, then runs them in a 3D engine, outperforming neural models on physical validi...
-
GenHSI: Controllable Generation of Human-Scene Interaction Videos
GenHSI is a training-free three-stage pipeline that turns a scene image, character image, and complex HSI prompt into long videos with plausible chained interactions by generating atomic actions, 3D keyframes via 2D i...
-
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
CCDM uses attribute-decoupled LoRA with relevance-guided aggregation and controllable regional context synthesis to support incremental concept customization in diffusion models while mitigating catastrophic forgettin...
-
DrawVideo: Generating Long Video from Storyboard Keyframe Sketches
DrawVideo is a sketch-guided framework that decomposes long videos into controllable shots using keyframe sketches, appearance prompts, and motion prompts, supported by a new SketchLongVideo dataset.
-
MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
MAVEN is a multi-agent prompt refinement framework that improves cultural fidelity in text-to-video generation, demonstrated on a new benchmark of 243 prompts and 972 videos across Chinese, American, and Romanian cultures.
-
MAVEN A Multi-Agent Framework for Multicultural Text-to-Video Generation
MAVEN introduces a multi-agent system for refining prompts in multicultural text-to-video generation and releases a benchmark of 243 prompts and 972 videos showing improved cultural relevance via parallel agent specia...
-
CineAGI: Character-Consistent Movie Creation through LLM-Orchestrated Multi-Modal Generation and Cross-Scene Integration
CineAGI is a multi-agent LLM framework that generates multi-scene movies with improved character consistency, narrative coherence, and audio-visual alignment.
-
ProPhy: Progressive Physical Alignment for Dynamic World Simulation
ProPhy adds explicit physics-aware conditioning via semantic and refinement experts plus VLM knowledge transfer to produce more physically coherent dynamic videos than prior methods.
-
Rolling Forcing: Autoregressive Long Video Diffusion in Real Time
Rolling Forcing generates multi-minute videos in real time by jointly denoising frames at increasing noise levels, anchoring attention to early frames, and using windowed distillation to limit error accumulation.
-
Infinite Worlds with Versatile Interactions
An open-source causal video world model sustains hour-long, 720p/60fps interactive generation without visual drift, paired with a VLM-based director-pilot agentic harness for rich, open-ended interaction.
-
DramaDirector: Geometry-Guided Short Drama Generation
DramaDirector retrieves depth-pose references from real drama shots to guide first-frame and image-to-video synthesis for plot-driven short dramas, paired with the DramaBoard benchmark.
-
One Sentence, One Drama: Personalized Short-Form Drama Generation via Multi-Agent Systems
A hierarchical multi-agent framework converts a single sentence into a short drama using debate-based scripting, 3D-grounded first frames for spatial consistency, and multi-stage reviewer loops.
-
Self-Reasoning Agentic Framework for Narrative Product Grid-Collage Generation
A self-reasoning agentic framework constructs a Product Narrative Framework, generates constraint-aware unified grid collages, and refines outputs via failure attribution to improve narrative coherence and aesthetics ...
-
ViMax: Agentic Video Generation
ViMax introduces a hierarchical multi-agent framework for long-form video generation that uses retrieval-augmented narrative planning and dependency-aware visual state tracking to maintain coherence across scenes.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.