DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects

• URL: http://arxiv.org/abs/2511.06115v1
• Date: Sat, 08 Nov 2025 19:50:53 GMT
• Title: DiLO: Disentangled Latent Optimization for Learning Shape and Deformation in Grouped Deforming 3D Objects
• Authors: Mostofa Rafid Uddin, Jana Armouti, Umong Sain, Md Asib Rahman, Xingjian Li, Min Xu,
• Abstract summary: We propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner.<n>Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques.<n>We demonstrate several significant downstream applications of our method, including unsupervised deformation transfer, deformation classification, and explainability analysis.
• Score: 7.47662549725561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a disentangled latent optimization-based method for parameterizing grouped deforming 3D objects into shape and deformation factors in an unsupervised manner. Our approach involves the joint optimization of a generator network along with the shape and deformation factors, supported by specific regularization techniques. For efficient amortized inference of disentangled shape and deformation codes, we train two order-invariant PoinNet-based encoder networks in the second stage of our method. We demonstrate several significant downstream applications of our method, including unsupervised deformation transfer, deformation classification, and explainability analysis. Extensive experiments conducted on 3D human, animal, and facial expression datasets demonstrate that our simple approach is highly effective in these downstream tasks, comparable or superior to existing methods with much higher complexity.

Related papers

• Generalizing Shape-from-Template to Topological Changes [3.9171668133637314]
  We propose a principled extension of Shape-from-Template (SfT) methods to reconstruct surfaces of deformable objects.<n>Our approach is iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional.<n>We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces.
  arXiv  Detail & Related papers  (2025-11-05T13:32:05Z)
• Efficient Autoregressive Shape Generation via Octree-Based Adaptive Tokenization [68.07464514094299]
  Existing methods encode all shapes into a fixed-size token, disregarding the inherent variations in scale and complexity across 3D data.<n>We introduce Octree-based Adaptive Tokenization, a novel framework that adjusts the dimension of latent representations according to shape complexity.<n>Our approach reduces token counts by 50% compared to fixed-size methods while maintaining comparable visual quality.
  arXiv  Detail & Related papers  (2025-04-03T17:57:52Z)
• CNS-Edit: 3D Shape Editing via Coupled Neural Shape Optimization [56.47175002368553]
  This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space. First, we design the coupled neural shape representation for supporting 3D shape editing. Second, we formulate the coupled neural shape optimization procedure to co-optimize the two coupled components in the representation subject to the editing operation.
  arXiv  Detail & Related papers  (2024-02-04T01:52:56Z)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation [66.16525145765604]
  We introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D registration task as a denoising diffusion process, which progressively refines the pose of the source point cloud. Experiments demonstrate that our diffusion registration framework presents outstanding pose estimation performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.
  arXiv  Detail & Related papers  (2023-10-26T12:47:26Z)
• Interpretable 2D Vision Models for 3D Medical Images [47.75089895500738]
  This study proposes a simple approach of adapting 2D networks with an intermediate feature representation for processing 3D images. We show on all 3D MedMNIST datasets as benchmark and two real-world datasets consisting of several hundred high-resolution CT or MRI scans that our approach performs on par with existing methods.
  arXiv  Detail & Related papers  (2023-07-13T08:27:09Z)
• IKOL: Inverse kinematics optimization layer for 3D human pose and shape estimation via Gauss-Newton differentiation [44.00115413716392]
  This paper presents an inverse kinematic layer (IKOL) for 3D human pose shape estimation. IKOL has a much over over than most existing regression-based methods. It provides a more accurate range of 3D human pose estimation.
  arXiv  Detail & Related papers  (2023-02-02T12:43:29Z)
• Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation [54.09274684734721]
  We present a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets. We may jointly train an encoder network to learn a latent space for inverting shapes, allowing us to enable a rich variety of whole-shape and region-aware shape manipulations.
  arXiv  Detail & Related papers  (2023-02-01T02:47:53Z)
• Neural Shape Deformation Priors [14.14047635248036]
  We present Neural Shape Deformation Priors, a novel method for shape manipulation. We learn the deformation behavior based on the underlying geometric properties of a shape. Our method can be applied to challenging deformations and generalizes well to unseen deformations.
  arXiv  Detail & Related papers  (2022-10-11T17:03:25Z)
• Real-Time Topology Optimization in 3D via Deep Transfer Learning [0.0]
  We introduce a transfer learning method based on a convolutional neural network. We show it can handle high-resolution 3D design domains of various shapes and topologies. Our experiments achieved an average binary accuracy of around 95% at real-time prediction rates.
  arXiv  Detail & Related papers  (2021-02-11T21:09:58Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.