LIMP: Learning Latent Shape Representations with Metric Preservation Priors

• URL: http://arxiv.org/abs/2003.12283v2
• Date: Wed, 2 Sep 2020 13:14:57 GMT
• Title: LIMP: Learning Latent Shape Representations with Metric Preservation Priors
• Abstract summary: We advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes. Our rationale lies in the observation that training samples alone are often insufficient to endow generative models with high fidelity. We demonstrate, for the first time, the adoption of differentiable intrinsic distances in the backpropagation of a geodesic loss.
• Score: 16.337713166628944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes. Key to our construction is the introduction of a geometric distortion criterion, defined directly on the decoded shapes, translating the preservation of the metric on the decoding to the formation of linear paths in the underlying latent space. Our rationale lies in the observation that training samples alone are often insufficient to endow generative models with high fidelity, motivating the need for large training datasets. In contrast, metric preservation provides a rigorous way to control the amount of geometric distortion incurring in the construction of the latent space, leading in turn to synthetic samples of higher quality. We further demonstrate, for the first time, the adoption of differentiable intrinsic distances in the backpropagation of a geodesic loss. Our geometric priors are particularly relevant in the presence of scarce training data, where learning any meaningful latent structure can be especially challenging. The effectiveness and potential of our generative model is showcased in applications of style transfer, content generation, and shape completion.

Related papers

• From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data [56.41479248637621]
  We introduce a novel 3D representation learning paradigm, namely textbfPRISM, for textbfPre-training.<n>PRISM learns embeddings by textbfRecovering the textbfIntrinsic textbfSurface geodesic textbfMetric.<n> Experiments demonstrate that our approach shows satisfactory accuracy, robustness, and high efficiency in geodesic distance prediction.
  arXiv  Detail & Related papers  (2026-06-01T13:54:26Z)
• Uncovering the Latent Potential of Deep Intermediate Representations [16.988545433223933]
  We show that task-relevant information is distributed non-monotonically across layers and cannot be recovered by nave aggregation.<n>We introduce Layer-wise Optimal Embedding Selection (LOES), a constructive spectral method that identifies task-discriminative subspaces.<n>Our method reveals how semantic factors are distributed across layers, thereby enabling cross-lingual and cross-modal interpretability analyses.
  arXiv  Detail & Related papers  (2026-05-21T20:58:42Z)
• High-Fidelity Single-Image Head Modeling with Industry-Grade Topology [7.585051164429312]
  Our framework adopts a coarse-to-fine optimization pipeline that refines a rigged template across three stages.<n>To mitigate the ill-posed nature of single-image 3D face reconstruction, we employ a normal consistency objective jointly with landmark alignment.<n>Our hierarchical optimization with geometry-aware regularization yields meshes with semantically meaningful edge flow and industry-grade topology.
  arXiv  Detail & Related papers  (2026-05-06T06:07:35Z)
• Simba: Towards High-Fidelity and Geometrically-Consistent Point Cloud Completion via Transformation Diffusion [31.34032485865941]
  We introduce Simba, a novel framework that reformulates point-wise transformation regression as a distribution learning problem.<n>Our approach integrates symmetry priors with the powerful generative capabilities of diffusion models, avoiding instance-specific memorization.
  arXiv  Detail & Related papers  (2025-11-20T09:02:42Z)
• Latent Iterative Refinement Flow: A Geometric-Constrained Approach for Few-Shot Generation [5.062604189239418]
  We introduce Latent Iterative Refinement Flow (LIRF), a novel approach to few-shot generation.<n>LIRF establishes a stable latent space using an autoencoder trained with our novel textbfmanifold-preservation loss.<n>Within this cycle, candidate samples are refined by a geometric textbfcorrection operator, a provably contractive mapping.
  arXiv  Detail & Related papers  (2025-09-24T08:57:21Z)
• Calibrating Biased Distribution in VFM-derived Latent Space via Cross-Domain Geometric Consistency [52.52950138164424]
  We show that when leveraging the off-the-shelf (vision) foundation models for feature extraction, the geometric shapes of the resulting feature distributions exhibit remarkable transferability across domains and datasets.<n>We embody our geometric knowledge-guided distribution calibration framework in two popular and challenging settings: federated learning and long-tailed recognition.<n>In long-tailed learning, it utilizes the geometric knowledge transferred from sample-rich categories to recover the true distribution for sample-scarce tail classes.
  arXiv  Detail & Related papers  (2025-08-19T05:22:59Z)
• Geometry-Editable and Appearance-Preserving Object Compositon [67.98806888489385]
  General object composition (GOC) aims to seamlessly integrate a target object into a background scene with desired geometric properties.<n>Recent approaches derive semantic embeddings and integrate them into advanced diffusion models to enable geometry-editable generation.<n>We introduce a Disentangled Geometry-editable and Appearance-preserving Diffusion model that first leverages semantic embeddings to implicitly capture desired geometric transformations.
  arXiv  Detail & Related papers  (2025-05-27T09:05:28Z)
• Follow the Energy, Find the Path: Riemannian Metrics from Energy-Based Models [63.331590876872944]
  We propose a method for deriving Riemannian metrics directly from pretrained Energy-Based Models.<n>These metrics define spatially varying distances, enabling the computation of geodesics.<n>We show that EBM-derived metrics consistently outperform established baselines.
  arXiv  Detail & Related papers  (2025-05-23T12:18:08Z)
• Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling [6.34618828355523]
  AI-driven surrogate modeling has become an increasingly effective alternative to physics-based simulations for 3D design, analysis, and manufacturing. This work introduces a self-supervised geometric representation learning method designed to capture fine-scale geometric features from non-parametric 3D models.
  arXiv  Detail & Related papers  (2025-04-27T17:10:13Z)
• ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction [50.07671826433922]
  It is non-trivial to simultaneously recover meticulous geometry and preserve smoothness across regions with differing characteristics. We propose ND-SDF, which learns a Normal Deflection field to represent the angular deviation between the scene normal and the prior normal. Our method not only obtains smooth weakly textured regions such as walls and floors but also preserves the geometric details of complex structures.
  arXiv  Detail & Related papers  (2024-08-22T17:59:01Z)
• GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image [94.56927147492738]
  We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes from single images. We show that leveraging diffusion priors can markedly improve generalization, detail preservation, and efficiency in resource usage. We propose a simple yet effective strategy to segregate the complex data distribution of various scenes into distinct sub-distributions.
  arXiv  Detail & Related papers  (2024-03-18T17:50:41Z)
• Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model [10.316008740970037]
  Generalizable models for implicit shape reconstruction from unoriented point cloud suffer from generalization issues. We propose here an efficient mechanism to remedy some of these limitations at test time. We demonstrate the improvement obtained through our method with respect to baselines and the state-of-the-art using synthetic and real data.
  arXiv  Detail & Related papers  (2023-11-21T20:12:29Z)
• Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measures [2.167843405313757]
  3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. Many generative learning algorithms require correspondence between each point when comparing the predicted shape and the target shape. We propose an architecture able to cope with different parameterizations, even during the training phase.
  arXiv  Detail & Related papers  (2023-06-27T19:27:15Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
  We propose to model latent structures with a learned dynamic potential landscape. Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations. Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T15:53:45Z)
• On the Viability of Monocular Depth Pre-training for Semantic Segmentation [48.29060171161375]
  We study whether pre-training on geometric tasks is viable for downstream transfer to semantic tasks. We find that monocular depth is a viable form of pre-training for semantic segmentation, validated by improvements over common baselines.
  arXiv  Detail & Related papers  (2022-03-26T04:27:28Z)
• GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning [54.291331971813364]
  offline reinforcement learning approaches can be divided into proximal and uncertainty-aware methods. In this work, we demonstrate the benefit of combining the two in a latent variational model. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data.
  arXiv  Detail & Related papers  (2021-02-22T19:42:40Z)

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.