RigidFormer: Learning Rigid Dynamics using Transformers | Paper Citation Record

Observation 0d5d41bc-714a-4fe2-be8c-6168ffbb4534 · outbound

This paper cites Learning rigid dynamics with face interaction graph networks.

RigidFormer: Learning Rigid Dynamics using Transformers Learning rigid dynamics with face interaction graph networks

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.892748Z

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Observation 2aa67b51-0567-42f0-a068-00bc3322c491 · outbound

This paper cites Genesis: A universal and generative physics engine for robotics and beyond.

RigidFormer: Learning Rigid Dynamics using Transformers Genesis: A universal and generative physics engine for robotics and beyond

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.579894Z

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Observation 37e15897-5b44-4902-8e25-a3396b66200c · outbound

This paper cites Interaction networks for learning about objects, relations and physics.Advances in neural information processing systems, 29.

RigidFormer: Learning Rigid Dynamics using Transformers Interaction networks for learning about objects, relations and physics.Advances in neural information processing systems, 29

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.644667Z

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Observation ad844f94-6010-4fc8-b399-ccec4c37c973 · outbound

This paper cites Deep regression on manifolds: a 3d rotation case study.

RigidFormer: Learning Rigid Dynamics using Transformers Deep regression on manifolds: a 3d rotation case study

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.622078Z

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Observation c87cd3e4-3b22-411e-9ac6-f38c6914d566 · outbound

This paper cites SE3-Nets: Learning rigid body motion using deep neural networks.

RigidFormer: Learning Rigid Dynamics using Transformers SE3-Nets: Learning rigid body motion using deep neural networks

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:15.666832Z

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Observation 76b86b2f-cd05-403f-b2d8-2d86fa8ffe6a · outbound

This paper cites A Compositional Object-Based Approach to Learning Physical Dynamics.

RigidFormer: Learning Rigid Dynamics using Transformers A Compositional Object-Based Approach to Learning Physical Dynamics

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.916940Z

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Observation bbfd4ef6-12e5-4a06-818b-0184c7c15289 · outbound

This paper cites Virtual elastic objects.

RigidFormer: Learning Rigid Dynamics using Transformers Virtual elastic objects

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.685697Z

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Observation 54e649f7-7806-4a50-a432-b7f538a0b196 · outbound

This paper cites Learning Neural Event Functions for Ordinary Differential Equations.

RigidFormer: Learning Rigid Dynamics using Transformers Learning Neural Event Functions for Ordinary Differential Equations

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.909561Z

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Observation acd37efc-1a19-4633-9354-4d40aa28d23e · outbound

This paper cites Pybullet, a python module for physics simulation for games, robotics and machine learning.

RigidFormer: Learning Rigid Dynamics using Transformers Pybullet, a python module for physics simulation for games, robotics and machine learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.648073Z

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Observation f16ebe8d-5219-4b9b-80fa-ea8ece7c06ab · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

RigidFormer: Learning Rigid Dynamics using Transformers Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.640826Z

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Observation 7135d664-6a18-4e3a-ac5d-b9be6758984f · outbound

This paper cites Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation.

RigidFormer: Learning Rigid Dynamics using Transformers Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.905621Z

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Observation c5ae0abe-09c1-44cc-bed0-8139b4557e6f · outbound

This paper cites Fast r-cnn.

RigidFormer: Learning Rigid Dynamics using Transformers Fast r-cnn

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.655940Z

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Observation d0bf41ed-4245-4c9e-89f0-49875b03f897 · outbound

This paper cites Clustering to minimize the maximum intercluster distance.Theoretical computer science, 38:293–306.

RigidFormer: Learning Rigid Dynamics using Transformers Clustering to minimize the maximum intercluster distance.Theoretical computer science, 38:293–306

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.689033Z

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Observation 1049b8d8-c436-411d-b84f-0d7740245cf7 · outbound

This paper cites an unresolved cited work.

RigidFormer: Learning Rigid Dynamics using Transformers Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-05-12T23:21:56.594654Z

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Observation fa0a8fbc-95cf-4f3f-afa4-2cc5c81a1f95 · outbound

This paper cites Rotary position embedding for vision transformer.

RigidFormer: Learning Rigid Dynamics using Transformers Rotary position embedding for vision transformer

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.591050Z

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Observation 85fa477f-aa74-449a-9c01-6975045d1cb9 · outbound

This paper cites DiffTaichi: Differentiable Programming for Physical Simulation.

RigidFormer: Learning Rigid Dynamics using Transformers DiffTaichi: Differentiable Programming for Physical Simulation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.888824Z

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Observation 247dc636-976d-44a2-b194-b6d74549aa29 · outbound

This paper cites arXiv preprint arXiv:2601.03782 (2026).

RigidFormer: Learning Rigid Dynamics using Transformers arXiv preprint arXiv:2601.03782 (2026)

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:21:16.913137Z

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Observation 20d93421-6bc3-46a1-a6ba-1694f367ac23 · outbound

This paper cites A solution for the best rotation to relate two sets of vectors.Foundations of Crystallography, 32(5):922–923.

RigidFormer: Learning Rigid Dynamics using Transformers A solution for the best rotation to relate two sets of vectors.Foundations of Crystallography, 32(5):922–923

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.608646Z

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Observation a16d1101-26c7-49ec-9683-37b7e87a1517 · outbound

This paper cites Object dynamics modeling with hierarchical point cloud-based representations.

RigidFormer: Learning Rigid Dynamics using Transformers Object dynamics modeling with hierarchical point cloud-based representations

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.563590Z

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Observation 7eb30562-3fc6-4402-8f3f-02245e83f7b2 · outbound

This paper cites Attention Residuals.

RigidFormer: Learning Rigid Dynamics using Transformers Attention Residuals

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:39:04.582456Z

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Observation 5a16705b-ece6-41b0-a695-0076b29954d7 · outbound

This paper cites Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds.

RigidFormer: Learning Rigid Dynamics using Transformers Mosca: Dynamic gaussian fusion from casual videos via 4d motion scaffolds

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.567159Z

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Observation d393a8a9-cf5f-4f86-9d78-a62c0c8b8d29 · outbound

This paper cites Decoupled weight decay regularization.

RigidFormer: Learning Rigid Dynamics using Transformers Decoupled weight decay regularization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.671098Z

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Observation c2dc37af-b214-43c3-9c9a-a50ddd658854 · outbound

This paper cites Warp: A high-performance python framework for gpu simulation and graphics.

RigidFormer: Learning Rigid Dynamics using Transformers Warp: A high-performance python framework for gpu simulation and graphics

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.604666Z

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Observation 8247794e-3a2f-4450-bd63-24ae8be5aa79 · outbound

This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

RigidFormer: Learning Rigid Dynamics using Transformers Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-12T21:46:21.566811Z

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Observation 247a3888-70b3-4d3d-92a6-1585a125b870 · outbound

This paper cites Mimickit: A reinforcement learning framework for motion imitation and control.

RigidFormer: Learning Rigid Dynamics using Transformers Mimickit: A reinforcement learning framework for motion imitation and control

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.935873Z

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Observation 736f4a7b-8b41-4de9-8d86-c319583476b8 · outbound

This paper cites Amp: Adversarial motion priors for stylized physics-based character control.ACM Transactions on Graphics (ToG), 40(4):1–20.

RigidFormer: Learning Rigid Dynamics using Transformers Amp: Adversarial motion priors for stylized physics-based character control.ACM Transactions on Graphics (ToG), 40(4):1–20

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.663013Z

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Observation 70beceea-16df-4255-bb7d-311a66d7138a · outbound

This paper cites Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters.ACM Transactions On Graphics (TOG), 41(4):1–17.

RigidFormer: Learning Rigid Dynamics using Transformers Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters.ACM Transactions On Graphics (TOG), 41(4):1–17

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.612803Z

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Observation 0818f408-600b-4573-848d-19ecce864dd6 · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer.

RigidFormer: Learning Rigid Dynamics using Transformers FiLM: Visual reasoning with a general conditioning layer

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.636954Z

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Observation 6ce65a52-d005-41a2-a4fb-ca00408d4c41 · outbound

This paper cites Learning mesh- based simulation with graph networks.

RigidFormer: Learning Rigid Dynamics using Transformers Learning mesh- based simulation with graph networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.692144Z

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Observation 56da8852-bc2d-448c-bff1-bd96f901cc97 · outbound

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

RigidFormer: Learning Rigid Dynamics using Transformers Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.598742Z

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Observation 883a3d00-16a2-44ee-a0f9-a8c6683fdb8a · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30.

RigidFormer: Learning Rigid Dynamics using Transformers Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.575339Z

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Observation 54d58277-a2c2-4cba-90b5-e89ed8ef434c · outbound

This paper cites Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free.

RigidFormer: Learning Rigid Dynamics using Transformers Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:04:34.965110Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 98da547b-14b2-4c67-ab04-2d6f4e641076 · outbound

This paper cites Learning rigid-body simulators over implicit shapes for large- scale scenes and vision.Advances in Neural Information Processing Systems, 37:125809– 125838.

RigidFormer: Learning Rigid Dynamics using Transformers Learning rigid-body simulators over implicit shapes for large- scale scenes and vision.Advances in Neural Information Processing Systems, 37:125809– 125838

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.583657Z

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Observation dcf1997b-7802-4083-9450-d6acb0df1098 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063.

RigidFormer: Learning Rigid Dynamics using Transformers Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.633301Z

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Observation 88a50912-8123-4ed2-ab51-0ec637a5297f · outbound

This paper cites Mujoco: A physics engine for model-based control.

RigidFormer: Learning Rigid Dynamics using Transformers Mujoco: A physics engine for model-based control

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.625441Z

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Observation fa672602-2495-4cb2-bcf3-9cd5887432d4 · outbound

This paper cites Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039.

RigidFormer: Learning Rigid Dynamics using Transformers Lion: Latent point diffusion models for 3d shape generation.Advances in Neural Information Processing Systems, 35:10021–10039

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.615906Z

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Observation 7c1fa0b9-baf7-4667-8989-e47d1af18ac0 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30.

RigidFormer: Learning Rigid Dynamics using Transformers Attention is all you need.Advances in neural information processing systems, 30

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.675106Z

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Observation 471daf49-cf25-45e7-8811-0ecf55778859 · outbound

This paper cites 6-PACK: Category-level 6D pose tracker with anchor-based keypoints.

RigidFormer: Learning Rigid Dynamics using Transformers 6-PACK: Category-level 6D pose tracker with anchor-based keypoints

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.695224Z

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Observation 462209a2-a626-47f3-8583-c8d447becb05 · outbound

This paper cites Monocular visual-inertial odometry in low-textured environments with smooth gradients: A fully dense direct ﬁltering approach.

RigidFormer: Learning Rigid Dynamics using Transformers Monocular visual-inertial odometry in low-textured environments with smooth gradients: A fully dense direct ﬁltering approach

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:21:15.672778Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e7165f81-1cdb-484b-aed7-2b20a0ff576c · outbound

This paper cites Tracking everything everywhere all at once.

RigidFormer: Learning Rigid Dynamics using Transformers Tracking everything everywhere all at once

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.666960Z

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Observation dcb77510-77e9-4bf3-b682-24dea48b0f6a · outbound

This paper cites Integrating physics and topology in neural networks for learning rigid body dynamics.Nature Communications, 16(1):6867.

RigidFormer: Learning Rigid Dynamics using Transformers Integrating physics and topology in neural networks for learning rigid body dynamics.Nature Communications, 16(1):6867

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.659532Z

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Observation 0a29f1a9-549f-4b09-9be5-d96fd1664cc2 · outbound

This paper cites Learning 3D Particle-based Simulators from RGB-D Videos.

RigidFormer: Learning Rigid Dynamics using Transformers Learning 3D Particle-based Simulators from RGB-D Videos

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.928032Z

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Observation a062e1e4-9456-4854-8c87-812235f814de · outbound

This paper cites Modeling the Real World with High-Density Visual Particle Dynamics.

RigidFormer: Learning Rigid Dynamics using Transformers Modeling the Real World with High-Density Visual Particle Dynamics

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.943406Z

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Observation 57c6731a-ff98-49b2-9325-f7e803c3dd38 · outbound

This paper cites Pointflow: 3d point cloud generation with continuous normalizing flows.

RigidFormer: Learning Rigid Dynamics using Transformers Pointflow: 3d point cloud generation with continuous normalizing flows

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.628946Z

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 612a8e7e-538d-47c9-bfa5-ab86cd078522 · outbound

This paper cites Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer.

RigidFormer: Learning Rigid Dynamics using Transformers Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.920889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f10c502-d77a-478b-9c15-ca8310a2f192 · outbound

This paper cites Egode: An event-attended graph ode framework for modeling rigid dynamics.Advances in Neural Information Processing Systems, 37:59093–59118.

RigidFormer: Learning Rigid Dynamics using Transformers Egode: An event-attended graph ode framework for modeling rigid dynamics.Advances in Neural Information Processing Systems, 37:59093–59118

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.570919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c5161cb2-a977-451c-9997-e49ed60d8688 · outbound

This paper cites Renderformer: Transformer- based neural rendering of triangle meshes with global illumination.

RigidFormer: Learning Rigid Dynamics using Transformers Renderformer: Transformer- based neural rendering of triangle meshes with global illumination

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.587041Z

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Observation 5dd94847-27cc-47b9-bb84-beb7d937df20 · outbound

This paper cites MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion.

RigidFormer: Learning Rigid Dynamics using Transformers MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:41:13.559959Z

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Observation b4264813-e345-4091-af41-a92f29862a8d · outbound

This paper cites Point transformer.

RigidFormer: Learning Rigid Dynamics using Transformers Point transformer

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.682118Z

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Observation 05f28094-eb7d-4135-a90a-2f9dc5729c1b · outbound

This paper cites TesserAct: Learning 4D Embodied World Models.

RigidFormer: Learning Rigid Dynamics using Transformers TesserAct: Learning 4D Embodied World Models

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:21:16.932112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2fdcaed7-d187-44c0-b624-b7694e7ed7a2 · outbound

This paper cites Extending lagrangian and hamiltonian neural networks with differentiable contact models.Advances in Neural Information Processing Systems, 34:21910–21922.

RigidFormer: Learning Rigid Dynamics using Transformers Extending lagrangian and hamiltonian neural networks with differentiable contact models.Advances in Neural Information Processing Systems, 34:21910–21922

Reference 51

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This paper cites the decoder object tokens.

RigidFormer: Learning Rigid Dynamics using Transformers the decoder object tokens

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.619110Z

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RigidFormer: Learning Rigid Dynamics using Transformers Unresolved cited work

Reference 53

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Observation 21c3ded2-3e8d-47d3-a34f-d27e4bb2e163 · outbound

This paper cites Meshes exceeding vertex limits undergo quadric decimation.

RigidFormer: Learning Rigid Dynamics using Transformers Meshes exceeding vertex limits undergo quadric decimation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T23:21:56.601784Z

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