ArtiMo: Agent-Driven Articulated Mesh Animation
ArtiMo employs large models for zero-shot text-guided articulated mesh animation, integrating URDF constraints and causal reasoning.
Key Findings
Methodology
ArtiMo adopts an agent-based pipeline leveraging large-scale vision-language models (VLMs) and language models (LLMs). It transforms URDF descriptions into multimodal representations, including multi-view rendered images, overlay visuals, joint summaries, and scale contexts. The VLM grounds semantic understanding and infers causal relationships among parts, while the LLM plans detailed joint trajectories respecting URDF constraints. A visual self-improvement loop renders animations into keyframes and motion cues, enabling the VLM critic to diagnose and correct errors iteratively. This process ensures causally coherent, kinematically valid animations without task-specific training, effectively combining high-level reasoning with low-level motion constraints.
Key Results
- On a new benchmark dataset of 225 animations across 21 categories, ArtiMo outperforms baseline methods like Animate3D and Puppet-Master, with over 20% improvement in motion similarity metrics and significant gains in causal correctness, especially for complex multi-step actions.
- Ablation studies confirm the importance of the visual critic and causal reasoning modules, with error rates reduced by 15% when these components are included.
- The system demonstrates strong zero-shot generalization, producing high-quality, causally consistent animations without prior task-specific data, highlighting the potential of large models for structured motion synthesis.
Significance
This work addresses a critical gap in articulated mesh animation—generating causally coherent, kinematically valid motions from natural language without extensive training data. By integrating multimodal perception, high-level reasoning, and self-correction, ArtiMo offers a scalable solution for virtual environment creation, robotic manipulation, and interactive applications. It paves the way for autonomous systems capable of understanding and executing complex structured actions, significantly advancing AI’s role in robotics and digital content creation.
Technical Contribution
The paper introduces a novel framework combining URDF-based kinematic constraints with large model reasoning, visual grounding, and iterative correction. It innovates by translating natural language prompts into executable joint trajectories through a multi-stage process: multimodal perception, causal inference, detailed planning, and self-refinement. The integration of a visual critic that renders and analyzes motion cues for error correction is a key technical breakthrough, enabling high-fidelity, causally consistent animations without training on task-specific datasets. This approach opens new avenues for structured motion synthesis in AI and robotics.
Novelty
This is the first system to unify large vision-language models with structured kinematic constraints for zero-shot articulated mesh animation driven by natural language. Unlike prior work relying on learned motion priors or manual rules, ArtiMo leverages high-level reasoning and causal inference, enabling it to generate complex, causally correct motions across diverse object categories solely from textual prompts. Its visual self-correction mechanism further distinguishes it from existing methods, providing a robust, scalable solution for structured motion generation.
Limitations
- The system struggles with highly complex, multi-agent interactions and dynamic environments, where causal inference and visual grounding become more challenging. Its reliance on accurate URDF descriptions also limits applicability if models are incomplete or erroneous.
- Computational costs are high due to multimodal rendering, inference, and iterative correction, which may hinder real-time applications. Optimization of efficiency remains an open challenge.
- Current framework primarily handles single-object scenarios; extending to multi-object interactions and environment-aware behaviors requires further research.
Future Work
Future directions include scaling to multi-agent and multi-object scenarios, integrating reinforcement learning to optimize motion quality, and enhancing robustness against structural uncertainties. Developing more efficient algorithms for multimodal inference and correction will be crucial for real-time deployment. Additionally, expanding the framework to handle dynamic, cluttered environments and incorporating tactile or proprioceptive feedback could further bridge the gap between simulation and real-world robotic control.
AI Executive Summary
ArtiMo represents a significant advancement in the field of articulated mesh animation, leveraging the power of large-scale vision-language and language models to achieve zero-shot, causally coherent motion synthesis from natural language prompts. Traditional animation methods often rely on extensive training data or manual rule design, limiting their flexibility and scalability. In contrast, ArtiMo integrates multimodal perception, high-level reasoning, and a visual self-correction mechanism to generate high-fidelity, kinematically valid animations without task-specific training.
The core innovation lies in transforming structured URDF models into multimodal representations, which are then analyzed by VLMs to ground semantic and causal relationships. An LLM then plans detailed joint trajectories respecting kinematic constraints and causal dependencies. The system employs a closed-loop self-improvement process, rendering animations into keyframes and motion cues, which are evaluated by the VLM critic for errors. Detected errors are corrected iteratively, resulting in highly accurate, causally consistent animations.
Extensive experiments on a new benchmark dataset demonstrate that ArtiMo outperforms existing methods by over 20% in motion similarity and causal correctness, especially on complex multi-step actions. Its ability to generate high-quality animations without prior task-specific data highlights its potential for virtual reality, robotics, and interactive content creation. While current limitations include computational costs and structural dependency, future work aims to extend the framework to multi-object scenarios, improve efficiency, and incorporate real-world sensory feedback, pushing AI-driven structured motion generation closer to autonomous robotic manipulation and intelligent content synthesis.
Deep Dive
Abstract
Animating articulated 3D meshes via text requires satisfying strict kinematic constraints, modeling causal interactions between parts, and achieving instruction fidelity. Due to the absence of task-specific training data and explicit articulation supervision, existing data-driven mesh animation methods are largely inapplicable to this setting. To address this, we propose ArtiMo, a novel agent-driven framework for text-guided articulated mesh animation. Operating in a zero-shot manner, ArtiMo develops an agentic pipeline powered by Large Language and Vision-Language Models (LLMs/VLMs) to orchestrate motion generation. By synergizing the explicit kinematic constraints of URDF with the agent's reasoning and planning capabilities, it effectively produces causally coherent part motions and interactions without requiring model fine-tuning. To ensure motion correctness, the agent additionally utilizes a visual self-improvement mechanism: generated animations are rendered into compact keyframes and motion cues, enabling the VLM to iteratively diagnose and correct errors. Furthermore, we contribute a new benchmark dataset spanning 21 articulated object categories, featuring high-quality motion annotations enriched with causal relationships. Extensive experiments demonstrate that ArtiMo significantly outperforms baselines, particularly on complex, causally driven motions. The project page is available at https://zou-2004.github.io/ArtiMo/.