Geometric Disentanglement for Generative Latent Shape Models | Paper Citation Record

Observation 8a3c027a-b060-45e1-9fd8-67ea74560a18 · outbound

This paper cites Learning Representations and Generative Models for 3D Point Clouds.

Geometric Disentanglement for Generative Latent Shape Models Learning Representations and Generative Models for 3D Point Clouds

Reference 1

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Observation 4c04f4da-4d4e-45ed-9472-740038b0797f · outbound

This paper cites Hyperprior Induced Unsupervised Disentanglement of Latent Representations.

Geometric Disentanglement for Generative Latent Shape Models Hyperprior Induced Unsupervised Disentanglement of Latent Representations

Reference 2

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Observation 8c575288-08b5-47d9-a68f-f93e0321b964 · outbound

This paper cites Point Convolutional Neural Networks by Extension Operators.

Geometric Disentanglement for Generative Latent Shape Models Point Convolutional Neural Networks by Extension Operators

Reference 3

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Observation 2b9f9af6-8f54-4119-9c6f-41a21e5236ba · outbound

This paper cites Layer Normalization.

Geometric Disentanglement for Generative Latent Shape Models Layer Normalization

Reference 4

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Observation d0a2ded9-83df-44df-ac37-792a91eb853d · outbound

This paper cites Possible principles underlying the transformation of sensory messages.

Geometric Disentanglement for Generative Latent Shape Models Possible principles underlying the transformation of sensory messages

Reference 5

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Observation 2e241023-bc4a-4e99-a3f0-a51cf7f71626 · outbound

This paper cites Constructing laplace operator from point clouds in Rd.

Geometric Disentanglement for Generative Latent Shape Models Constructing laplace operator from point clouds in Rd

Reference 6

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Observation baa6e6ee-d303-4e19-a153-c341d9aff57a · outbound

This paper cites Rep- resentation learning: A review and new perspectives.

Geometric Disentanglement for Generative Latent Shape Models Rep- resentation learning: A review and new perspectives

Reference 7

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Observation 20fd950a-d090-4912-b36d-2af7416e4233 · outbound

This paper cites Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations.

Geometric Disentanglement for Generative Latent Shape Models Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

Reference 8

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Observation 6aa27e08-dbf8-4d27-8161-cea6facd7a8f · outbound

This paper cites Shape google: Geometric words and expressions for invariant shape retrieval.

Geometric Disentanglement for Generative Latent Shape Models Shape google: Geometric words and expressions for invariant shape retrieval

Reference 9

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Observation 957ab805-e256-43c9-8098-26502bde0fc4 · outbound

This paper cites Understanding disentangling in $\beta$-VAE.

Geometric Disentanglement for Generative Latent Shape Models Understanding disentangling in $\beta$-VAE

Reference 10

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Observation c23ed47a-3692-4434-8612-5f0e93f161d3 · outbound

This paper cites Isolating Sources of Disentanglement in Variational Autoencoders.

Geometric Disentanglement for Generative Latent Shape Models Isolating Sources of Disentanglement in Variational Autoencoders

Reference 11

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Observation ad722701-3451-408e-a06a-5299cfccd735 · outbound

This paper cites Infogan: Interpretable repre- sentation learning by information maximizing generative ad- versarial nets.

Geometric Disentanglement for Generative Latent Shape Models Infogan: Interpretable repre- sentation learning by information maximizing generative ad- versarial nets

Reference 12

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Observation 836a3eeb-ad02-4705-a0bb-215d979d9193 · outbound

This paper cites Functional char- acterization of intrinsic and extrinsic geometry.

Geometric Disentanglement for Generative Latent Shape Models Functional char- acterization of intrinsic and extrinsic geometry

Reference 13

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Observation 6163ca58-9346-4d85-a055-d21a1697bae6 · outbound

This paper cites Learning to sam- ple.

Geometric Disentanglement for Generative Latent Shape Models Learning to sam- ple

Reference 14

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Observation ed146b0e-6a92-4b9b-aae6-8a88a3d88d34 · outbound

This paper cites Structured Disentangled Representations.

Geometric Disentanglement for Generative Latent Shape Models Structured Disentangled Representations

Reference 15

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Observation d055cbf6-772b-4586-8660-53b21d0adaf6 · outbound

This paper cites Multi-view face detection using deep convolutional neural networks.

Geometric Disentanglement for Generative Latent Shape Models Multi-view face detection using deep convolutional neural networks

Reference 16

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Observation 69e06551-12d5-43bb-9b8a-23d4773e4b9b · outbound

This paper cites Automatic unpaired shape deformation transfer.

Geometric Disentanglement for Generative Latent Shape Models Automatic unpaired shape deformation transfer

Reference 17

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Observation d26b809a-6a50-48e7-a7d8-b5493e43dc62 · outbound

This paper cites Auto-Encoding Total Correlation Explanation.

Geometric Disentanglement for Generative Latent Shape Models Auto-Encoding Total Correlation Explanation

Reference 18

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Observation 933a8723-cd5c-4541-80f9-b7fd6e2284fe · outbound

This paper cites Generative adversarial nets.

Geometric Disentanglement for Generative Latent Shape Models Generative adversarial nets

Reference 19

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Observation 03a83d2f-b1e6-462e-8815-9e5385333a16 · outbound

This paper cites Atlasnet: A papier-mache ap- proach to learning 3d surface generation.

Geometric Disentanglement for Generative Latent Shape Models Atlasnet: A papier-mache ap- proach to learning 3d surface generation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.471199Z

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Observation 6f7ec09d-6a39-4128-a1da-6f6206b3ed54 · outbound

This paper cites 3d-coded: 3d cor- respondences by deep deformation.

Geometric Disentanglement for Generative Latent Shape Models 3d-coded: 3d cor- respondences by deep deformation

Reference 21

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Observation 87e5a08d-4f33-4a2b-b838-690bf4771ba3 · outbound

This paper cites A two-step disentanglement method.

Geometric Disentanglement for Generative Latent Shape Models A two-step disentanglement method

Reference 22

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Observation 36a342ad-4705-4020-8a47-12c93bbc51f3 · outbound

This paper cites Disentangling Latent Factors of Variational Auto-Encoder with Whitening.

Geometric Disentanglement for Generative Latent Shape Models Disentangling Latent Factors of Variational Auto-Encoder with Whitening

Reference 23

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Observation b6fc368b-277a-4158-b565-5c333830e1ad · outbound

This paper cites Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds.

Geometric Disentanglement for Generative Latent Shape Models Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:52:05.904847Z

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Observation 3e95698f-075b-4294-bc74-b7648f804362 · outbound

This paper cites Early Visual Concept Learning with Unsupervised Deep Learning.

Geometric Disentanglement for Generative Latent Shape Models Early Visual Concept Learning with Unsupervised Deep Learning

Reference 25

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Observation b328676c-504c-47cc-9886-6f802324e1a6 · outbound

This paper cites β-vae: Learning basic visual concepts with a constrained variational framework.

Geometric Disentanglement for Generative Latent Shape Models β-vae: Learning basic visual concepts with a constrained variational framework

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.433755Z

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Observation 87fc9605-fbc1-47e0-b58f-c32cc8dafc9d · outbound

This paper cites The role of independent motion in object segmentation in the ventral visual stream: Learning to recognise the separate parts of the body.

Geometric Disentanglement for Generative Latent Shape Models The role of independent motion in object segmentation in the ventral visual stream: Learning to recognise the separate parts of the body

Reference 27

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Observation 7d7bb86c-55ae-4b9a-bcc2-90ec338dbf96 · outbound

This paper cites Group-based Learning of Disentangled Representations with Generalizability for Novel Contents.

Geometric Disentanglement for Generative Latent Shape Models Group-based Learning of Disentangled Representations with Generalizability for Novel Contents

Reference 28

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Observation 138be15e-e123-4922-afc1-73aef0d6ae1c · outbound

This paper cites Metrics for 3d rotations: Comparison and analysis.

Geometric Disentanglement for Generative Latent Shape Models Metrics for 3d rotations: Comparison and analysis

Reference 29

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Observation c38b7bf3-7042-4afb-8fb0-fa840e5c0de2 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Geometric Disentanglement for Generative Latent Shape Models Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 30

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Observation b1cee368-9f87-40b6-b176-d97913b66397 · outbound

This paper cites Rotation invariant spherical harmonic repre- sentation of 3d shape descriptors.

Geometric Disentanglement for Generative Latent Shape Models Rotation invariant spherical harmonic repre- sentation of 3d shape descriptors

Reference 31

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Observation f1d3cfe0-062b-4701-9ff9-b0cfaa79a3dc · outbound

This paper cites Disentangling by Factorising.

Geometric Disentanglement for Generative Latent Shape Models Disentangling by Factorising

Reference 32

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Observation feaa5762-8cf4-47a8-ba58-7f52169b7c46 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Geometric Disentanglement for Generative Latent Shape Models Adam: A Method for Stochastic Optimization

Reference 33

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Observation 8bc6bc65-6526-44bd-83b5-9acab35a8a9e · outbound

This paper cites Auto-Encoding Variational Bayes.

Geometric Disentanglement for Generative Latent Shape Models Auto-Encoding Variational Bayes

Reference 34

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Observation 8415d904-b315-4027-91ee-15d3b551c1bf · outbound

This paper cites Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.

Geometric Disentanglement for Generative Latent Shape Models Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Reference 35

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Observation 4ac7613e-c68e-4529-b357-bd373b8aeab6 · outbound

This paper cites Autoencoding beyond pixels using a learned similarity metric.

Geometric Disentanglement for Generative Latent Shape Models Autoencoding beyond pixels using a learned similarity metric

Reference 36

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Observation 436bfa9a-7a0d-4eaf-84bd-5652558c9c28 · outbound

This paper cites Gradient-based learning applied to document recog- nition.

Geometric Disentanglement for Generative Latent Shape Models Gradient-based learning applied to document recog- nition

Reference 37

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Observation 5106bab5-2b1a-43d6-9ba4-ad66da5727d4 · outbound

This paper cites Point Cloud GAN.

Geometric Disentanglement for Generative Latent Shape Models Point Cloud GAN

Reference 38

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Observation 2af6ff0c-ad5c-4bb3-95f2-60ccfff3178a · outbound

This paper cites So-net: Self- organizing network for point cloud analysis.

Geometric Disentanglement for Generative Latent Shape Models So-net: Self- organizing network for point cloud analysis

Reference 39

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Observation c96ebaf6-8ab3-4311-99bc-eb7ef71a673a · outbound

This paper cites an unresolved cited work.

Geometric Disentanglement for Generative Latent Shape Models Unresolved cited work

Reference 40

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Observation d51f4ae5-44cf-4f9c-9833-1fdcaf3539f7 · outbound

This paper cites Visualiz- ing data using t-sne.

Geometric Disentanglement for Generative Latent Shape Models Visualiz- ing data using t-sne

Reference 41

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Observation c50beb68-af0e-41e8-89c6-b7fc9cb2445e · outbound

This paper cites Adversarial variational bayes: Unifying variational autoen- coders and generative adversarial networks.

Geometric Disentanglement for Generative Latent Shape Models Adversarial variational bayes: Unifying variational autoen- coders and generative adversarial networks

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.326142Z

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Observation 715dd4e2-46d9-4ab5-93f6-bd6101b1723c · outbound

This paper cites Discrete differential-geometry operators for triangu- lated 2-manifolds.

Geometric Disentanglement for Generative Latent Shape Models Discrete differential-geometry operators for triangu- lated 2-manifolds

Reference 43

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Observation 55410e68-ee33-4253-b77b-0651c752a7e0 · outbound

This paper cites Learning disentangled representations with semi-supervised deep generative mod- els.

Geometric Disentanglement for Generative Latent Shape Models Learning disentangled representations with semi-supervised deep generative mod- els

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.297891Z

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Observation 19fbc6dd-aea9-4287-8ecc-5c906397dfe0 · outbound

This paper cites The shape variational autoencoder: A deep generative model of part-segmented 3d objects.

Geometric Disentanglement for Generative Latent Shape Models The shape variational autoencoder: A deep generative model of part-segmented 3d objects

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.283291Z

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Observation c19cfd19-c891-4a2d-8baf-01847d38339a · outbound

This paper cites Automatic differentiation in pytorch.

Geometric Disentanglement for Generative Latent Shape Models Automatic differentiation in pytorch

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.269033Z

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Observation 7acf1038-e6ce-4ae3-be1d-78bb5d2f3fd9 · outbound

This paper cites Scikit-learn: Machine Learning in Python.

Geometric Disentanglement for Generative Latent Shape Models Scikit-learn: Machine Learning in Python

Reference 47

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Observation 24e36816-87bb-4af3-a176-75c43abccd8a · outbound

This paper cites Shape retrieval of non-rigid 3d human models.International Journal of Computer Vision, 120(2):169–193, 2016.

Geometric Disentanglement for Generative Latent Shape Models Shape retrieval of non-rigid 3d human models.International Journal of Computer Vision, 120(2):169–193, 2016

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.254689Z

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Observation 8e81c5ef-c3ef-41be-ac3e-83b86194c49c · outbound

This paper cites an unresolved cited work.

Geometric Disentanglement for Generative Latent Shape Models Unresolved cited work

Reference 49

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Observation b87534b3-3cb1-4607-bdae-04c76381c18e · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classiﬁca- tion and segmentation.

Geometric Disentanglement for Generative Latent Shape Models Pointnet: Deep learning on point sets for 3d classiﬁca- tion and segmentation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.225802Z

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Observation a511c113-9efa-484e-a3b0-86c059e7e703 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

Geometric Disentanglement for Generative Latent Shape Models Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.209974Z

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Observation a5956331-7e1a-44dc-8010-7bfe4bbb85e8 · outbound

This paper cites Deep Learning with Sets and Point Clouds.

Geometric Disentanglement for Generative Latent Shape Models Deep Learning with Sets and Point Clouds

Reference 52

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Observation ba8a9e0a-7b9b-4f74-8f81-6fdb56fd68fd · outbound

This paper cites Laplace–beltrami spectra as shape-dna of surfaces and solids.

Geometric Disentanglement for Generative Latent Shape Models Laplace–beltrami spectra as shape-dna of surfaces and solids

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.195476Z

Source-reported events for the cited work

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Observation 4d46a5c0-17a7-49cd-99bd-9adac77fb206 · outbound

This paper cites Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Geometric Disentanglement for Generative Latent Shape Models Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-14T12:52:05.593598Z

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Observation 71576653-c227-450b-888f-ed61b6b7eb34 · outbound

This paper cites Learning Disentangled Representations with Reference-Based Variational Autoencoders.

Geometric Disentanglement for Generative Latent Shape Models Learning Disentangled Representations with Reference-Based Variational Autoencoders

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.181142Z

Source-reported events for the cited work

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Observation 82b879bf-95be-43e0-8210-109a61ac1cd9 · outbound

This paper cites Vari- ational autoencoders for deforming 3d mesh models.

Geometric Disentanglement for Generative Latent Shape Models Vari- ational autoencoders for deforming 3d mesh models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.166266Z

Source-reported events for the cited work

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Observation 7795909f-0529-422a-8ae9-6e8089ade7d4 · outbound

This paper cites VAE with a VampPrior.

Geometric Disentanglement for Generative Latent Shape Models VAE with a VampPrior

Reference 57

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This paper cites Spectral geometry process- ing with manifold harmonics.

Geometric Disentanglement for Generative Latent Shape Models Spectral geometry process- ing with manifold harmonics

Reference 58

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This paper cites Learn- ing localized generative models for 3d point clouds via graph convolution.

Geometric Disentanglement for Generative Latent Shape Models Learn- ing localized generative models for 3d point clouds via graph convolution

Reference 59

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Observation 28ad8cc4-1588-4a42-8ae2-c35aade2566e · outbound

This paper cites Black, Ivan Laptev, and Cordelia Schmid.

Geometric Disentanglement for Generative Latent Shape Models Black, Ivan Laptev, and Cordelia Schmid

Reference 60

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This paper cites Information theoretical analysis of multi- variate correlation.

Geometric Disentanglement for Generative Latent Shape Models Information theoretical analysis of multi- variate correlation

Reference 61

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This paper cites SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters.

Geometric Disentanglement for Generative Latent Shape Models SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

Reference 62

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This paper cites Fold- ingnet: Point cloud auto-encoder via deep grid deformation.

Geometric Disentanglement for Generative Latent Shape Models Fold- ingnet: Point cloud auto-encoder via deep grid deformation

Reference 63

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This paper cites The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models.

Geometric Disentanglement for Generative Latent Shape Models The Information Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Models

Reference 64

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This paper cites T-networks.

Geometric Disentanglement for Generative Latent Shape Models T-networks

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.082608Z

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This paper cites -R”), or using an LBO estimated from a point cloud (denoted “-P.

Geometric Disentanglement for Generative Latent Shape Models -R”), or using an LBO estimated from a point cloud (denoted “-P

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:52:06.066892Z

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