Generative Animations: A Multi-Model Pipeline for Prompt-Driven Motion Synthesis

Problem
This paper addresses the gap in automated animation generation from natural language prompts, a task that traditionally requires significant manual effort from designers. The authors highlight the limitations of existing methods that rely on preset animations and manual adjustments, which can be time-consuming and lack flexibility. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is a multi-model pipeline that integrates Large Language Models (LLMs) for semantic parsing with the Segment Anything Model (SAM) for visual grounding. The pipeline operates in several stages: first, it interprets natural language prompts to extract semantic meaning; then, it generates motion paths that adhere to the geometry of the scene, accounting for depth-based occlusions and 3D perspective transformations. The authors employ a combination of LLMs for understanding user intent and SAM for accurately mapping the generated animations onto the visual context. The training compute specifics are not disclosed, but the architecture leverages existing models to facilitate the animation generation process.

Results
The authors demonstrate the effectiveness of their system through three distinct use cases: contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion on transformed objects. While specific quantitative results are not provided in the abstract, the authors claim that their approach significantly reduces the manual effort required for animation creation compared to traditional methods. They benchmark their system against standard animation techniques, although exact performance metrics and comparisons to named baselines are not detailed in the provided text.

Limitations
The authors acknowledge several limitations, including potential challenges in accurately interpreting complex prompts and the reliance on the quality of the underlying models (LLMs and SAM). They also note that the system may struggle with highly intricate scenes or prompts that require nuanced understanding beyond the current capabilities of LLMs. An additional limitation not explicitly mentioned is the potential computational overhead associated with chaining multiple models, which could impact real-time performance in practical applications.

Why it matters
This work has significant implications for the fields of animation and interactive media, as it proposes a novel approach to automating the animation process, thereby democratizing access to animation tools for non-experts. By enabling users to generate animations through simple natural language prompts, the system could streamline workflows in various domains, including education, marketing, and entertainment. Furthermore, the integration of LLMs with visual grounding models opens avenues for future research in multimodal AI, particularly in enhancing the interpretability and usability of generative systems.

Authors: Mannat Khurana, Sanyam Jain, Rishav Agarwal
Source: arXiv:2605.27203
URL: https://arxiv.org/abs/2605.27203v1