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FusionDeformer: text-guided mesh deformation using diffusion models
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AI
DOI：10.1007/s00371-024-03463-7.png)
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Abstract
En 中文
Mesh deformation has a wide range of applications, including character creation, geometry modelling, deforming animation, and morphing. Recently, mesh deformation methods based on CLIP models demonstrated the ability to perform automatic text-guided mesh deformation. However, using 2D guidance to deform a 3D mesh attempts to solve an ill-posed problem and leads to distortion and unsmoothness, which cannot be eliminated by CLIP-based methods because they focus on semantic-aware features and cannot identify these artefacts. To this end, we propose FusionDeformer, a novel automatic text-guided mesh deformation method that leverages diffusion models. The deformation is achieved by Score Distillation Sampling, which minimizes the KL-divergence between the distribution of rendered deformed mesh and the text-conditioned distribution. To alleviate the intrinsic ill-posed problem, we incorporate two approaches into our framework. The first approach involves combining multiple orthogonal views into a single image, providing robust deformation while avoiding the need for additional memory. The second approach incorporates a new regularization to address the unsmooth artefacts. Our experimental results show that the proposed method can generate high-quality, smoothly deformed meshes that align precisely with the input text description while preserving the topological relationships. Additionally, our method offers a text2morphing approach to animation design, enabling common users to produce special effects animation.
Keywords:
Diffusion model
Mesh deformation
Score Distillation Sampling