Story3D-Agent: Exploring 3D Storytelling Visualization with Large Language Models
원문 초록
Traditional visual storytelling is complex, requiring specialized knowledge and substantial resources, yet often constrained by human creativity and creation precision. While Large Language Models (LLMs) enhance visual storytelling, current approaches often limit themselves to 2D visuals or oversimplify stories through motion synthesis and behavioral simulation, failing to create comprehensive, multi-dimensional narratives. To this end, we present Story3D-Agent, a pioneering approach that leverages the capabilities of LLMs to transform provided narratives into 3D-rendered visualizations. By integrating procedural modeling, our approach enables precise control over multi-character actions and motions, as well as diverse decorative elements, ensuring the long-range and dynamic 3D representation. Furthermore, our method supports narrative extension through logical reasoning, ensuring that generated content remains consistent with existing conditions. We have thoroughly evaluated our Story3D-Agent to validate its effectiveness, offering a basic framework to advance 3D story representation.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Motion-R1: Chain-of-Thought Reasoning and Reinforcement Learning for Human Motion Generation
DeepGesture: A conversational gesture synthesis system based on emotions and semantics
VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions
DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
Consistent Story Generation with Asymmetry Zigzag Sampling
토론
이 논문과 연결된 포럼 글이 아직 없습니다.