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arxiv: 2605.28394 · v1 · pith:LKW4BQHDnew · submitted 2026-05-27 · 💻 cs.CV · cs.GR

Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization

Gaurav Rai , Ojaswa Sharma This is my paper

Pith reviewed 2026-06-29 13:19 UTC · model grok-4.3

classification 💻 cs.CV cs.GR
keywords sketch animationtext-driven motiondiffusion-guided optimizationskeleton optimization3D character animationscore distillation samplinglinear blend skinningphysics constraints
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The pith

A text-to-video diffusion model guides skeleton optimization to turn 2D sketches into text-aligned 3D animations without paired motion data.

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

The paper introduces a framework that animates hand-drawn 2D sketches into 3D character motions using only a text description. It represents motion through skeletal transformations that deform a mesh via linear blend skinning, then optimizes those transformations with guidance from a diffusion model. This guidance comes from motion-aware score-distillation sampling that pulls the animation toward realistic and semantically matching movement. Physical constraints on smoothness, topology, and contact plus a spring-mass simulator keep the results plausible. A reader would care because the approach works across biped, quadruped, and non-living characters and removes the need for large paired motion datasets.

Core claim

The paper claims that motion-aware score-distillation sampling from a text-to-video diffusion model can steer the optimization of skeletal transformations, which are then applied to meshes through linear blend skinning, while physics-inspired smoothness, topological, and contact constraints plus a spring-mass simulator stabilize the process, yielding temporally coherent and text-aligned 3D animations from 2D sketches for diverse articulated characters without any paired motion training data.

What carries the argument

Motion-aware score-distillation sampling (MoSDS) that uses a text-to-video diffusion model to provide gradients for optimizing skeletal joint transformations.

If this is right

  • The same pipeline produces animations for bipedal, quadrupedal, and non-living articulated characters.
  • Adding the spring-mass simulator introduces secondary motion effects on top of the primary skeletal animation.
  • The optimization remains stable under explicit smoothness, topological, and contact constraints.
  • The generated sequences are temporally coherent and better aligned with input text than motion transfer baselines that lack generative priors.
  • The full system is modular and fully differentiable, allowing substitution of different diffusion models or skinning methods.

Where Pith is reading between the lines

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

  • The method could be adapted to accept rough 3D scans instead of 2D sketches by replacing the initial skeleton estimation step.
  • Because the diffusion guidance operates on rendered video frames, the same loop might be applied to other parametric animation representations such as blend shapes.
  • Extending the contact constraints to handle multiple interacting characters would test whether the framework scales to scene-level animation.
  • The absence of paired data training suggests the approach could serve as a zero-shot initializer for later fine-tuning on small custom datasets.

Load-bearing premise

Motion-aware score-distillation sampling from a text-to-video diffusion model can effectively guide skeleton optimization to produce realistic and semantically meaningful motion without any paired motion data.

What would settle it

Running the skeleton optimization loop on a set of sketches and text prompts and finding that the resulting animations receive lower human ratings for text alignment and motion realism than the same skeletons optimized without the diffusion term or with only the physical constraints.

Figures

Figures reproduced from arXiv: 2605.28394 by Gaurav Rai, Ojaswa Sharma.

Figure 1
Figure 1. Figure 1: Overview of our proposed method for 2D sketch to 3D animation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of pipeline architecture of our proposed method for 2D sketch to 3D model animation. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative results of our proposed method and generated 3D animation sequences from input text prompts. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative Comparison with state-of-the-art methods AnimateAnyMesh [11] and BiMotion [12]). [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual results of ablation study on different settings [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
read the original abstract

Animation of 2D hand-drawn sketches provides an effective medium for visual communication. However, these sketches pose challenges, particularly in handling occlusions and accurately mapping motion. While 3D animation naturally addresses these challenges, estimating 3D motion remains a very complex task. Recent approaches to converting 2D sketches to 3D animations have mainly focused on specific types of motion, such as bipedal movements and facial expressions. We propose Sketch2Motion, a diffusion-guided framework for skeleton-based motion synthesis that combines classical character animation pipelines with deep generative priors. Our method represents motion using skeletal transformations, which are propagated to mesh deformations via linear blend skinning. To guide the resulting animation toward realistic and semantically meaningful motion, we integrate a text-to-video diffusion model via motion-aware score-distillation sampling (MoSDS), enabling optimization without paired motion data. Additionally, we apply physics-inspired smoothness, topological, and contact constraints to stabilize optimization and preserve motion plausibility. Further, we integrate a spring-mass simulator to introduce secondary motion effects. The proposed framework is generalized, fully differentiable, modular, and compatible with biped, quadruped, and non-living articulated characters. Experiments demonstrate that our approach produces temporally coherent, text-aligned animations that outperform baseline motion transfer methods that lack generative priors or explicit physical constraints. We will make our code and dataset publicly available.

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.

Referee Report

1 major / 1 minor

Summary. The paper proposes Sketch2Motion, a diffusion-guided framework for skeleton-based motion synthesis from 2D sketches driven by text. It uses skeletal transformations propagated to mesh via linear blend skinning, guided by motion-aware score-distillation sampling (MoSDS) from a text-to-video diffusion model, with additional physics-inspired smoothness, topological, and contact constraints, and a spring-mass simulator for secondary motion. The framework is claimed to be generalized for different character types and to produce temporally coherent, text-aligned animations that outperform baseline methods lacking generative priors or physical constraints.

Significance. If the results hold, the work provides a modular, fully differentiable approach to text-driven 3D animation from sketches without paired motion data by combining classical character animation with deep generative priors. This could have impact in animation and visual communication fields. The approach avoids circularity by relying on external diffusion models and classical skinning.

major comments (1)
  1. [Abstract] Abstract: the claim that 'Experiments demonstrate that our approach produces temporally coherent, text-aligned animations that outperform baseline motion transfer methods' supplies no metrics, figures, ablation details, or experimental setup, so the data cannot be checked against the claim; this is load-bearing for the central empirical result.
minor comments (1)
  1. [Abstract] Abstract: the statement that code and dataset will be made publicly available should specify a repository or timing for the camera-ready version.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and constructive comment. We address the major concern regarding the abstract below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'Experiments demonstrate that our approach produces temporally coherent, text-aligned animations that outperform baseline motion transfer methods' supplies no metrics, figures, ablation details, or experimental setup, so the data cannot be checked against the claim; this is load-bearing for the central empirical result.

    Authors: We agree that the abstract claim would be stronger with additional context on the supporting evidence. The full paper provides quantitative results (user studies, motion quality metrics, and comparisons to baselines) in Section 4, along with ablations and figures. In the revised version, we will update the abstract to briefly reference these key evaluation aspects and point to the experimental section, while respecting length limits. This addresses the verifiability concern without altering the core claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper presents a modular pipeline that combines classical linear blend skinning and skeletal transforms with an external text-to-video diffusion model via motion-aware score-distillation sampling (MoSDS), plus independent physics-inspired constraints and a spring-mass simulator. No equations, assumptions, or experimental claims in the abstract or described method reduce the central result to a quantity defined by the method itself, a fitted parameter renamed as prediction, or a self-citation chain. The approach is explicitly positioned as using external generative priors and classical animation components, rendering the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract; no explicit free parameters, axioms, or invented entities are identifiable.

pith-pipeline@v0.9.1-grok · 5779 in / 1026 out tokens · 45114 ms · 2026-06-29T13:19:27.953742+00:00 · methodology

discussion (0)

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Reference graph

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