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

Mannat Khurana; Rishav Agarwal; Sanyam Jain

arxiv: 2605.27203 · v1 · pith:5LEMKGPGnew · submitted 2026-05-26 · 💻 cs.CV · cs.AI

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

Mannat Khurana , Sanyam Jain , Rishav Agarwal This is my paper

Pith reviewed 2026-06-29 18:35 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords generative animationsprompt-driven motion synthesislarge language modelssegment anything modelmotion pathsscene geometrynatural language promptsvisual grounding

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The pith

Chaining LLMs with SAM turns natural language prompts into motion paths that respect scene geometry and occlusions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a pipeline that converts text prompts into production-ready animations by first using large language models to parse the semantic intent of the description and then applying the Segment Anything Model to ground those elements visually within the actual image or scene. This chaining is meant to automate the creation of motion paths that automatically follow contours, manage depth-based occlusions, and account for 3D perspective without requiring designers to manually draw Bezier curves or adjust timing. Demonstrations cover contour-following trajectories, orbital motions aware of z-order, and movements aligned to perspective-transformed objects. A sympathetic reader would care because it promises to replace cumbersome manual animation setup with prompt-based generation while still producing outputs that obey the underlying scene structure.

Core claim

By chaining Large Language Models for semantic parsing with the Segment Anything Model for visual grounding, the pipeline automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms, as shown through three use cases of contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion on transformed objects.

What carries the argument

The multi-model pipeline chaining LLMs for semantic parsing with SAM for visual grounding to produce geometrically accurate motion paths from prompts.

If this is right

Designers can generate complex animations directly from text descriptions instead of manually selecting presets or plotting points.
Generated paths automatically respect scene geometry, depth occlusions, and 3D perspective transforms.
Three concrete demonstrations show contour-following, z-order-aware orbital motion, and perspective-aligned paths on transformed objects.
The approach eliminates separate configuration of timing properties by deriving them from the prompt and scene analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the pipeline generalizes, it could lower the skill threshold for creating custom motion in documents or interfaces for users without animation training.
The same chaining idea might extend to generating paths in video sequences or interactive 3D environments if the visual grounding step can be repeated across frames.
Failure cases where the LLM misparses spatial relations or SAM segments incorrectly would point to where additional validation steps become necessary.

Load-bearing premise

Off-the-shelf LLMs and SAM can be chained without custom training, error correction, or manual overrides to reliably output geometrically accurate motion paths from natural language prompts.

What would settle it

Test the pipeline on a scene containing known depth occlusions or perspective distortions and verify whether the output motion paths cross through occluded regions or violate perspective rules.

Figures

Figures reproduced from arXiv: 2605.27203 by Mannat Khurana, Rishav Agarwal, Sanyam Jain.

**Figure 1.** Figure 1: Generative Animations automatically project motion paths into the 3D coordinate space of the subject, ensuring geometrically [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Unified System Architecture: The pipeline translates nat [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 4.** Figure 4: Depth Extension: The system segments Earth to establish [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗

**Figure 5.** Figure 5: 3D Extension: (a) Input object with existing perspec [PITH_FULL_IMAGE:figures/full_fig_p003_5.png] view at source ↗

read the original abstract

Animation elevates digital documents into immersive experiences, yet creating custom motion paths remains cumbersome, requiring designers to manually select presets, plot B\'ezier points, and configure timing properties. We introduce Generative Animations, a system that transforms natural language prompts into production-ready animations. By chaining Large Language Models (LLMs) for semantic parsing with the Segment Anything Model (SAM) for visual grounding, our pipeline automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms. We demonstrate the system through three use cases: contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion on transformed objects.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper sketches an LLM-plus-SAM pipeline for turning text prompts into animation paths but supplies no mechanism or evidence for the claimed 3D geometry handling.

read the letter

The core of this work is a chained system: an LLM parses a natural-language prompt, SAM segments the relevant image regions, and the output is turned into motion paths for three example animations (contour following, orbital with z-order, and perspective-aligned). That chaining itself is the only concrete contribution; everything else re-uses existing models without modification.

The description of the three use cases is clear enough that a practitioner could probably re-implement the high-level flow. The paper does not claim new algorithms or training procedures, so the novelty is limited to the application domain.

The central problem is that the abstract asserts the paths “respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms,” yet the components listed do none of that. SAM produces 2D masks; standard LLMs produce text. No depth estimator, camera model, or projective transform is mentioned, and the manuscript contains no quantitative results, error metrics, or ablation data to show the claim holds in practice. Without those pieces the geometric guarantees rest on an unstated assumption that text reasoning plus 2D segmentation is sufficient.

Because the work is only a high-level system sketch with no evaluation, it is mainly of interest to engineers already building prompt-driven animation tools who want to see one possible wiring diagram. It does not contain enough substance for a serious referee to spend time on; the soundness gap is too large for the claims being made.

Referee Report

3 major / 0 minor

Summary. The manuscript introduces Generative Animations, a multi-model pipeline that chains LLMs for semantic parsing of natural language prompts with SAM for visual grounding to automatically produce motion paths for animations. It claims these paths respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms, and demonstrates the approach via three use cases: contour-following trajectories, orbital animations with z-order awareness, and perspective-aligned motion.

Significance. If the geometric guarantees could be substantiated, the work would offer a practical advance in reducing manual effort for custom animation paths in digital documents by leveraging off-the-shelf models. However, the complete absence of quantitative evaluation, error metrics, or implementation details prevents any assessment of whether the central claims hold.

major comments (3)

[Abstract] Abstract: the central claim that chaining LLMs and SAM 'automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms' is unsupported, as the manuscript supplies no modules, depth estimators, camera parameters, or projective routines that would convert 2D SAM masks into occlusion-aware or perspective-correct trajectories.
The manuscript contains no quantitative results, success rates, error metrics, ablation studies, or baseline comparisons to validate the assertion of automatic, geometrically correct path generation across the three use cases.
No description is given of the precise chaining procedure, prompt engineering, error-correction steps, or any post-processing that would be required to achieve the stated 3D capabilities from purely 2D segmentation and text reasoning.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed comments. We address each point below and propose revisions where appropriate to clarify the scope and limitations of our work.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that chaining LLMs and SAM 'automatically generates motion paths that respect scene geometry, handle depth-based occlusions, and honor 3D perspective transforms' is unsupported, as the manuscript supplies no modules, depth estimators, camera parameters, or projective routines that would convert 2D SAM masks into occlusion-aware or perspective-correct trajectories.

Authors: We acknowledge that the abstract overstates the automatic geometric capabilities. The pipeline uses LLMs to interpret prompts and generate high-level motion descriptions, and SAM to ground objects in 2D, with the 3D aspects being handled through specific heuristics in each use case rather than general modules. No depth estimators or projective routines are present. We will revise the abstract to tone down these claims to reflect what is actually demonstrated in the use cases. revision: yes
Referee: [—] The manuscript contains no quantitative results, success rates, error metrics, ablation studies, or baseline comparisons to validate the assertion of automatic, geometrically correct path generation across the three use cases.

Authors: The work is presented as a demonstration of a novel pipeline through three illustrative use cases rather than a comprehensive evaluation. Quantitative metrics would require a benchmark dataset and defined measures for geometric fidelity, which are not standard in this emerging area. We will add a limitations section discussing the lack of quantitative evaluation and the challenges in defining such metrics. revision: partial
Referee: [—] No description is given of the precise chaining procedure, prompt engineering, error-correction steps, or any post-processing that would be required to achieve the stated 3D capabilities from purely 2D segmentation and text reasoning.

Authors: The manuscript describes the overall pipeline in the methods section, but we agree that additional details on the exact LLM prompts, chaining logic, and any post-processing steps would improve reproducibility. We will expand the methods section with more precise descriptions, example prompts, and pseudocode for the procedure. revision: yes

Circularity Check

0 steps flagged

No circularity: high-level system description with no derivations or equations

full rationale

The paper presents a pipeline description chaining LLMs and SAM for animation generation but contains no equations, fitted parameters, mathematical derivations, or self-citations that could reduce any claim to its inputs by construction. The central claim about generating geometrically accurate paths is an assertion about system behavior rather than a derived result from prior steps within the paper. No load-bearing steps match any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model, free parameters, axioms, or invented entities appear in the abstract; the contribution is a descriptive engineering pipeline.

pith-pipeline@v0.9.1-grok · 5643 in / 1032 out tokens · 28457 ms · 2026-06-29T18:35:16.690672+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages

[1]

Amenta and M

[AB99] A MENTA N., B ERN M.: Surface reconstruction by V oronoi fil- tering. Discrete & Computational Geometry 22, 4 (1999), 481–504. doi:10.1007/PL00009475. 2 [Air17] A IRBNB DESIGN : Lottie: Render after effects animations na- tively. https://lottie.airbnb.tech/,

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[2]

Kirillov, E

2 [BMR∗20] B RO WN T., M ANN B., R YDER N., S UBBIAH M., K APLAN J. D., D HARIWAL P., N EELAKANTAN A., S HYAM P., S ASTRY G., ASKELL A., ET AL .: Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems (NeurIPS) (2020), vol. 33, pp. 1877–1901. 2 [JCL∗23] J IANG B., C HEN X., L IU W., Y U J., Y U G., C HEN T.: Mo- tion...

work page doi:10.1109/iccv51070.2023.00371 2020

[1] [1]

Amenta and M

[AB99] A MENTA N., B ERN M.: Surface reconstruction by V oronoi fil- tering. Discrete & Computational Geometry 22, 4 (1999), 481–504. doi:10.1007/PL00009475. 2 [Air17] A IRBNB DESIGN : Lottie: Render after effects animations na- tively. https://lottie.airbnb.tech/,

work page doi:10.1007/pl00009475 1999

[2] [2]

Kirillov, E

2 [BMR∗20] B RO WN T., M ANN B., R YDER N., S UBBIAH M., K APLAN J. D., D HARIWAL P., N EELAKANTAN A., S HYAM P., S ASTRY G., ASKELL A., ET AL .: Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems (NeurIPS) (2020), vol. 33, pp. 1877–1901. 2 [JCL∗23] J IANG B., C HEN X., L IU W., Y U J., Y U G., C HEN T.: Mo- tion...

work page doi:10.1109/iccv51070.2023.00371 2020