LION generates high-quality 3D shapes using hierarchical latent diffusion models.
problem Creating high-quality 3D shapes for digital artists.
method Hierarchical Latent Point Diffusion Model (LION) with a global shape latent and point-structured latent space.
result LION achieves state-of-the-art generation performance on ShapeNet benchmarks.
BézierGAN generates smooth curves from low-dimensional parameters.
problem Designing smooth curves for aerodynamic and hydrodynamic shapes.
method Generative model that maps low-dimensional latent representation to Bézier curve points.
result Generates diverse and realistic curves with consistent shape variation.
LIMP learns latent shapes with metric preservation, improving generative models.
problem Insufficient training data for high-fidelity latent representations.
method Metric preservation as a prior, geometric distortion criterion, geodesic loss.
result Synthetic samples of higher quality achieved through metric preservation.
We improve autoencoder image interpolation by shaping latent space.
problem Incongruities in autoencoder interpolation leading to artifacts or unrealistic results.
method Propose a regularization technique to shape latent space to follow a smooth, locally convex manifold consistent with training images.
result Faithful interpolation between data points achieved.
Generative model disentangles 3D shapes into independent factors.
problem Learning rich representations of deformable 3D shapes.
method Supervised 3D mesh-convolutional Variational AutoEncoder with latent feature disentanglement.
result Explicit disentanglement of latent factors improves shape generation and downstream tasks.
Paper uses VAEs to control IVS features for financial modeling.
problem Generating realistic IVSs with desired characteristics.
method Variational autoencoder architecture with controllable latent variables.
result Controlled generation of IVSs with specified features.
New method for surface analysis using restricted deformation bases.
problem Surface registration and comparison without pre-registered data.
method Elastic Riemannian metrics with basis-restricted transformations.
result Effective implementation on human body and face scans.
AI learns to classify and represent univariate distributions in a 2D latent space.
problem Classifying and representing univariate empirical distributions.
method Unsupervised beta variational autoencoder (beta-VAE) to separate and represent distributions in a 2D latent space.
result The latent space representation separates distributions of different shapes while overlapping similar ones.
3D Adversarial Autoencoder learns compact binary descriptors from 3D point clouds.
problem Learning meaningful representations of 3D shapes for various tasks.
method End-to-end Adversarial Autoencoder model trained on 3D input and output.
result 3D Adversarial Autoencoder (3dAAE) generates state-of-the-art results for 3D points clustering and retrieval.
Study improves interpretability in generative models by disentangling latent variables in scientific datasets.
problem Extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings.
method Introducing Aux-VAE, a novel architecture within the VAE framework, which disentangles latent variables by guiding them with auxiliary variables.
result Aux-VAE achieves disentanglement with minimal modifications to the standard VAE loss function, validated on multiple datasets.
A new method for generating realistic and creative 3D shapes from point clouds.
problem Generating realistic and creative 3D shapes from point clouds.
method Learning to interpolate point clouds by encoding prior knowledge about real-world objects.
result Generated 3D shapes are both realistic and creative, unlike any existing forms.
Generator network replicates AAM's face recognition neuron responses.
problem Replicating AAM's face recognition neuron responses using a deep generative model.
method Using a variational auto-encoder, learned generator network from face images generated by AAM, capturing shape variations without explicit shape model.
result Inferred latent variables of the learned generator network have strong linear relationship with AAM's shape and appearance variables.
Denoising improves autoencoder representations and synthesis.
problem Improving autoencoder representations from corrupted data.
method Integrates denoising and adversarial training to shape latent space.
result Denoising autoencoders achieve higher classification performance and more consistent samples.
ES-VAE models skeletal pose trajectories by removing nuisance factors.
problem Handling camera orientation, subject scale, viewpoint, and execution speed in skeletal data.
method ES-VAE uses TSRVF representation on Kendall's shape manifold to isolate shape dynamics.
result ES-VAE outperforms standard VAEs and sequence modeling baselines in gait cycle prediction and action recognition.
PointGMM learns hGMMs from point clouds for 3D shape representation.
problem Lack of shape priors and non-local information in point cloud representations.
method Neural network that learns hierarchical Gaussian mixture models (hGMMs) for 3D shapes.
result Generative model learns meaningful latent space for interpolations and novel shape synthesis.
Unified approach for learning state representations from streaming data.
problem Learning reusable state representations from high-dimensional, non-stationary data.
method Unified mathematical formulation for learning latent relations, enabling flexible and principled shaping of latent space.
result Improved understanding and evaluation of existing unsupervised learning approaches.
Proposes Structuring AutoEncoders to learn structured latent spaces.
problem Traditional Autoencoders fail to discover semantic structure in raw data.
method Enhances traditional Autoencoders using weak supervision to form a structured latent space.
result Structured latent space allows for more efficient data representation and tasks like classification.
Graph autoencoder learns molecule designs matching training data.
problem Learning to generate molecules that match training data statistics.
method Graph-structured variational autoencoder with sequential graph extension.
result Our model designs molecules that are locally optimal in desired properties.
Interprets how intrinsic motivation shapes behavior in RL agents.
problem Understanding how intrinsic motivation influences behavior in reinforcement learning agents.
method Analyzed five RL agents in procedurally generated environments using various interpretability techniques.
result Curiosity-driven agents exhibit broader and more dynamic attention than extrinsically motivated agents.
A new method generates implied volatility surfaces without arbitrage issues.
problem Generating volatility surfaces without financial arbitrage constraints.
method Variational autoencoder with flow-matching for latent space learning.
result Model closely reproduces empirical distribution and satisfies no-arbitrage conditions.
Generative model combines shape and intensity priors for left atrium segmentation.
problem Challenges in segmenting left atrium MRI images due to shape variation and multimodality.
method Generative image model with mixture of Gaussians for shape priors and autoencoders for intensity priors.
result Maximizes posterior probability using a mixture of Gaussians for shape priors and autoencoders for intensity priors.
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time ( t − 1 ) (t-1) ( t − 1 ) as the shape parameters of those at time t t t , which are linked …
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…
A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…
The LORACs prior improves latent representation interpretability in VAEs.
problem Learning interpretable latent representations in VAEs.
method Flexible Bayesian nonparametric hierarchical clustering prior based on TMC for VAEs.
result Improved interpretability and practical performance of latent space.
Model learns tool affordances from vision, enabling tool selection.
problem Learning tool affordances from visual input.
method Vision-based generative model with task predictor.
result Agents can select appropriate tools based on task success criteria.
Estimates latent positions in 1D torus from noisy pairwise affinities.
problem Estimating latent positions in a 1D torus from noisy pairwise affinities.
method Introduced an estimation procedure with provable localization error of O ( log ( n ) / n ) O(\sqrt{\log(n)/n}) O ( log ( n ) / n ) . result The estimation procedure provably localizes latent positions with a maximum error of O ( log ( n ) / n ) O(\sqrt{\log(n)/n}) O ( log ( n ) / n ) . Proposes a new layer for efficient 3D shape discrimination.
problem Irregular structure and redundancy in 3D point clouds hinder efficient inter-class discrimination.
method Integrates Blended Convolution and Synthesis layer that projects and synthesizes 3D point clouds, followed by 3D convolution in the unit ball.
result End-to-end architecture achieves compelling results on 3D shape recognition and retrieval.
Paper tackles unsupervised learning of 3D shapes from single images.
problem Learning 3D shapes from single images without supervision.
method Generative models, variational auto-encoders, adversarial methods.
result Model learns 3D shapes and poses from single images, showing potential for various datasets.
Generative model learns from designs to create new shapes.
problem Designing new shapes for conceptual optimization.
method Variational autoencoder-decoder architecture for 3D shape synthesis.
result Generator maps latent space to smooth 3D surfaces for optimization.
VCAE improves autoencoder quality on MNIST and CelebA.
problem Overfitting and poor generative/reconstruction quality in autoencoders.
method Proposes variance-constrained autoencoder (VCAE) to enforce variance constraint on latent distribution.
result VCAE outperforms Wasserstein Autoencoder and Variational Autoencoder in quality.
Sliced-Wasserstein Autoencoders simplify generative modeling.
problem Creating generative models without complex training networks.
method Regularize autoencoder loss with sliced-Wasserstein distance.
result Efficient implementation with similar capabilities to WAE and VAE.
Geomstats introduces shape module for analyzing shapes of objects.
problem Analyzing shapes of objects represented as landmarks, curves, and surfaces.
method Implementing shape spaces, group actions, fiber bundles, quotient spaces, and Riemannian metrics.
result Users can compare, average, and interpolate shapes inside shape spaces.
Presents STRIPE model for probabilistic forecasting of non-stationary time series.
problem Probabilistic forecasting of non-stationary time series.
method STRIPE model representing structured diversity based on shape and time features, with diversification mechanism using determinantal point processes (DPP).
result STRIPE significantly outperforms baseline methods for representing diversity while maintaining forecasting accuracy.
New approach reduces shape optimization anomalies and improves design quality.
problem Improving global optimization efficiency and avoiding geometrical anomalies in shape optimization.
method Reducing design variables, modeling generative process via probabilistic models, penalizing anomalous designs.
result Abnormal designs are penalized, leading to high-quality designs and improved convergence.
Kernel-based tests for shape constraints in finance.
problem Enforcing shape relations on latent functions in financial econometrics.
method Kernel-based nonparametric framework for mean-variance optimization.
result Established statistical properties and a joint Wald-type statistic for testing shape constraints.
DDMI generates high-quality INRs by adapting positional embeddings.
problem Existing INR generative models fail to produce high-quality representations.
method DDMI uses adaptive positional embeddings and a D2C-VAE to enhance expressive power.
result DDMI outperforms existing models across multiple modalities and datasets.
A model separates visual style from digit type on MNIST and facial features from shape on CelebA.
problem Learning compact, independent factors of data.
method Explicitly encoded in a generative model with two latent spaces: spatial transformations and intrinsic appearance.
result The model separates visual style from digit type on MNIST and facial features from shape on CelebA.
Generative model learns shape drift for quantifying domain uncertainty in hemodynamics.
problem Quantifying domain uncertainty in medical image segmentation for biomarker estimation.
method Conditional stochastic interpolant framework based on LDDMM registration.
result Generative model can create random perturbations of shapes for biomarker estimation.
Constructing VAE Latent Spaces with Prescribed Topology
problem Resolving topological mismatch in VAEs for non-Euclidean data
method A constructive framework for product covering spaces
result Topology-aware latent representations with closed-form KL divergences
Smooth generative model of shapes with uncertainty from silhouette images.
problem Challenges in modeling shapes represented as silhouette images due to intractable posteriors.
method Gaussian Process Deep Belief Networks (GPDBN) that learn from small data and propagate uncertainty.
result Proposed model provides favorable results compared to state-of-the-art models.
A new shape space allows optimization of non-smooth shapes in fluid mechanics.
problem Optimizing non-smooth shapes in fluid mechanics.
method Constructing a product manifold to include piecewise-smooth shapes.
result Numerical results show applicability in minimizing viscous energy dissipation.
In shape analysis, the concept of shape spaces has always been vague, requiring a case-by-case approach for every new type of shape. In this paper, we give a general definition for an abstract space of shapes in a manifold. This notion encompasses every shape space studied so far in the literature, and offers a rigorou…
Classifies compact spaces by shape, finite spaces by weak homotopy.
problem Classifying compact Hausdorff spaces and finite topological spaces.
method Constructs a category that classifies spaces by shape and weak homotopy.
result Classifies compact spaces by shape, finite spaces by weak homotopy.
VQShape learns interpretable time-series representations and achieves comparable performance to specialist models.
problem Lack of interpretability in existing time-series models.
method Vector quantization of time-series data into abstracted shapes.
result VQShape achieves comparable performance to specialist models in classification tasks.
Hausdorff reflection keeps space shape intact.
problem Preserving shape type in spaces.
method Hausdorff reflection method.
result Hausdorff reflection preserves shape type.
The paper explores three methods to assign a metric to shape spaces.
problem Assigning a Riemannian metric to shape spaces without parameterization.
method Three methods to put a Riemannian metric on shape spaces.
result Methods provide a way to measure deformations independent of parameterization.
A novel method predicts shape development using Riemannian shape spaces.
problem Predicting future shape development from a single observation.
method Proposes a novel prediction method that encodes shapes in a Riemannian shape space and learns hierarchical statistical models.
result Outperforms deep learning-supported variants and state-of-the-art methods in predicting shape development.