Skip to content

MeshSeqGen: Zero-shot Mesh Animation based on a Large Video Generation Model.

Sep 2026 · IEEE Transactions on Visualization and Computer Graphics · Vol PP · 0 citations
Medicine

TL;DR

MeshSeqGen is introduced, a zero-shot framework that simplifies this process by using the motions from generated video frames to guide mesh deformation, and its mesh-based Gaussian representation ensures the final animations are both plausible and structurally consistent.

Abstract

Motion generation for 3D meshes is a fundamental task in computer animation, yet traditional methods like keyframe animation and motion capture are often costly and resource-intensive. Motivated by the progress in large-scale 2D video generation models, we introduce MeshSeqGen, a zero-shot framework that simplifies this process by using the motions from generated video frames to guide mesh deformation. Our method significantly differs from prior video-to-4D techniques lacking mesh-specific structural consistency, and other meshbased methods that require extensive 4D training data. To bridge the gap between 2D videos and 3D meshes, we design a mesh deformation network that learns from a pre-trained large video generation model and a multi-view synthesis model as spatialtemporal consistency guidance, while our mesh-based Gaussian representation ensures the final animations are both plausible and structurally consistent. Extensive evaluations demonstrate the performance of our method over existing approaches by producing diverse motions and high-quality mesh animations from simple text prompts, and making the process user-friendly and highly efficient without the need for complex rigging or keyframing.

View source

Similar papers

Apr 2026

AnimateAnyMesh++: A Flexible Feed-Forward Framework for High-Fidelity Text-Driven Mesh Animation.

AnimateAnyMesh++ is presented, a feed-forward framework for text-driven animation of arbitrary 3D meshes with substantial upgrades in data, architecture, and generative capability, and demonstrates that AnimateAnyMesh++ generates semantically accurate and tem porally coherent mesh animations within seconds, surpassing...

Zijie Wu, Chao-Hui Yu, Fan Wang et al. · 3 citations · ⚡1
Preprint Aug 2026

BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives

We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh whose motion follows the video. Rather than relying on explicit rigs or directly regressing high-dimensional vertex motion, we represent anim...

Pradyumn Goyal, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu et al. · 0 citations
Open access Aug 2026

AnyTalk: Speech Animation for Arbitrary Characters Leveraging a Video Generation Model.

AnyTalk enables lip-synced animations across diverse face meshes and blendshape configurations, significantly reducing manual effort and data requirements and enhances usability by distilling AnyTalk into a streamlined network, $\text{AnyTalk}_{RT}$, thereby enabling real-time performance.

Kwan Yun, Serin Yoon, Sun-Jin Jung et al. · 0 citations
#diffusion models Conference Open access Sep 2026

AI-Driven 3D/4D Content Generation, Editing, and Animation Using Diffusion Models and Gaussian Splatting: A Narrative Review

This narrative review syn thesizes representative works published between 2023 and 2025 across four interconnected sub-areas: text-to-3D/4D generation, 3D scene editing, dynamic scene animation, and motion and video generation.

Yan-Ni Liu · 0 citations
Open access Sep 2026

PartLock: Vision–Language Model-Guided Part-Level Mesh Dragging

3D mesh dragging enables intuitive local shape editing by specifying a small number of handle points and their target positions, and has important applications in 3D content creation, animation, and industrial design. Existing diffusion-prior-based methods can improve the visual plausibility of large deformations, but...

Song-Sheng Wu, Liu-Hong Luo, Hong-Jun Li · 0 citations
Preprint Aug 2026

Wan-Animate-2: Pushing the Application Boundaries of Character Animation

Wan-Animate-2 is presented, an end-to-end character animation framework that directly consumes the driving video within a redesigned Diffusion Transformer and achieves superior motion fidelity and identity preservation by eliminating intermediate motion extractors entirely.

Guangyuan Wang, Liucheng Hu, Dechao Meng et al. · 3 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.