ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, 3D Shape Modeling · Depth: Expert, extended

Summary

ARAPDiffusion is a novel latent diffusion model designed to learn continuous shape spaces of deformable shape collections, such as humans, animals, and bones. Developed by researchers at the University of Texas at Austin and Westlake University, its key innovation lies in integrating as-rigid-as-possible (ARAP) deformation model regularization losses into the latent diffusion (LD) framework. This approach significantly reduces the need for extensive 3D training data, a common challenge in domains like medical imaging. ARAPDiffusion employs an alternating optimization procedure across three stages: initial pre-training of a generic LD model, fine-tuning the decoder with ARAP regularization using the LD model's latent distribution, and fine-tuning the LD model with regularization derived from the shape decoder. Experimental results on benchmark datasets demonstrate ARAPDiffusion's superior performance over baseline methods in unconditional and conditional shape generation, achieving improvements in reconstruction errors ($e_r$), quality metrics ($e_t$), and distribution alignment ($d_W$). For instance, it improved $e_r$ by 3.1% on Human and 3.9% on SMAL datasets.

Key takeaway

For Machine Learning Engineers developing generative models for deformable 3D shapes with limited datasets, ARAPDiffusion offers a robust solution. Its ARAP regularization and alternating optimization strategy significantly enhance shape quality and distribution alignment, reducing reliance on vast training data. You should consider integrating geometric priors and iterative refinement into your latent diffusion pipelines, especially for applications in medical imaging or specialized domains where data acquisition is challenging. This approach can yield more geometrically consistent and higher-fidelity generative models.

Key insights

ARAPDiffusion integrates geometric regularization into latent diffusion to learn deformable shape spaces with less data.

Principles

• ARAP regularization reduces 3D training data requirements.
• Alternating optimization refines both encoder/decoder and latent diffusion.
• Normalized deformation metrics improve regularization effectiveness.

Method

ARAPDiffusion pre-trains an autoencoder and latent diffusion model, then alternates fine-tuning the decoder using ARAP regularization from the diffusion model's latent distribution, and fine-tuning the diffusion model using a shape decoder-derived quality score.

In practice

• Apply ARAP regularization to diffusion models for sparse 3D data.
• Use a quality scoring function to guide latent diffusion training.

Topics

• Deformable Shape Generation
• Latent Diffusion Models
• ARAP Regularization
• 3D Generative Models
• Alternating Optimization
• Point Clouds

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.