Drive Any Mesh: 4D Latent Diffusion for Mesh Deformation from Video

• URL: http://arxiv.org/abs/2506.07489v1
• Date: Mon, 09 Jun 2025 07:08:58 GMT
• Title: Drive Any Mesh: 4D Latent Diffusion for Mesh Deformation from Video
• Authors: Yahao Shi, Yang Liu, Yanmin Wu, Xing Liu, Chen Zhao, Jie Luo, Bin Zhou,
• Abstract summary: DriveAnyMesh is a method for driving mesh guided by monocular video.<n>We present a 4D diffusion model that denoises sequences of latent sets.<n>Lashed sets leverage variational autoencoder, simultaneously capturing 3D shape and motion information.
• Score: 19.830248504692563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose DriveAnyMesh, a method for driving mesh guided by monocular video. Current 4D generation techniques encounter challenges with modern rendering engines. Implicit methods have low rendering efficiency and are unfriendly to rasterization-based engines, while skeletal methods demand significant manual effort and lack cross-category generalization. Animating existing 3D assets, instead of creating 4D assets from scratch, demands a deep understanding of the input's 3D structure. To tackle these challenges, we present a 4D diffusion model that denoises sequences of latent sets, which are then decoded to produce mesh animations from point cloud trajectory sequences. These latent sets leverage a transformer-based variational autoencoder, simultaneously capturing 3D shape and motion information. By employing a spatiotemporal, transformer-based diffusion model, information is exchanged across multiple latent frames, enhancing the efficiency and generalization of the generated results. Our experimental results demonstrate that DriveAnyMesh can rapidly produce high-quality animations for complex motions and is compatible with modern rendering engines. This method holds potential for applications in both the gaming and filming industries.

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