Paper Citation Record · LEDGER

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

As of 15 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 3 inbound Pith citation observations for arXiv:2411.14423.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.14423 v4

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:14:41.894668Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:29:49.747976Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T09:13:16.515188Z

Reference resolution

49 of 49 outbound references displayed

verified exact2
verified fuzzy29
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 284eec96-29a3-4564-97d0-09f49c7b2271 · outbound

This paper cites GPT-4 Technical Report.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 3cdb9235-3623-4016-9342-36deb782dd2d · outbound

This paper cites Mip-nerf 360: Unbounded anti-aliased neural radiance fields.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Mip-nerf 360: Unbounded anti-aliased neural radiance fields

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.852144Z

Source-reported events for the cited work

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Observation 6d963a4e-9d0c-4a52-89c0-76b0c9c2e4c2 · outbound

This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.528135Z

Source-reported events for the cited work

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Observation 90502584-a51d-4986-beea-57e1dffb9fe7 · outbound

This paper cites Gaussian- informed continuum for physical property identification and simulation.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Gaussian- informed continuum for physical property identification and simulation

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.835386Z

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Observation bd754748-a657-4b35-be36-82028b332356 · outbound

This paper cites DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.545761Z

Source-reported events for the cited work

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Observation 3e817269-3e5f-4dbd-a972-21e6060d083e · outbound

This paper cites PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.558606Z

Source-reported events for the cited work

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Observation c81d11f1-372d-4af0-a612-2d193b56ff85 · outbound

This paper cites GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation GigaGS: Scaling up Planar-Based 3D Gaussians for Large Scene Surface Reconstruction

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.565103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46e94b50-7acc-4a0a-a21b-bb4b59fb3764 · outbound

This paper cites Dream- scene4d: Dynamic multi-object scene generation from monocular videos.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Dream- scene4d: Dynamic multi-object scene generation from monocular videos

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.819588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bd18c3b9-b7a7-476c-b7a9-51a6bb365ea4 · outbound

This paper cites StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.580702Z

Source-reported events for the cited work

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Observation 23563879-5120-4684-82a2-7d77104beb57 · outbound

This paper cites Flownet: Learn- ing optical flow with convolutional networks.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Flownet: Learn- ing optical flow with convolutional networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.803595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9eac2bec-74f8-49bf-af4c-5faa4797d0a9 · outbound

This paper cites A point set generation network for 3d object reconstruction from a single image.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation A point set generation network for 3d object reconstruction from a single image

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.788075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2c130928-5538-4f3d-b8bb-76a277fb30b7 · outbound

This paper cites SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation SAM2Point: Segment Any 3D as Videos in Zero-shot and Promptable Manners

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.602856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de34ad4-7709-4b74-a416-63ecdd5e2c6a · outbound

This paper cites https://github.com/nvidia/warp.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation https://github.com/nvidia/warp

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.771902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9e57340c-ce43-498b-ba82-7ec48ded3c62 · outbound

This paper cites A moving least squares material point method with displacement disconti- nuity and two-way rigid body coupling.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation A moving least squares material point method with displacement disconti- nuity and two-way rigid body coupling

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.756265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6630e152-833f-49f6-803f-9b5a3cc7bd83 · outbound

This paper cites NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.623952Z

Source-reported events for the cited work

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Observation 06456792-6da6-4599-87a5-b2a0fc5cad6d · outbound

This paper cites DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation DreamPhysics: Learning Physics-Based 3D Dynamics with Video Diffusion Priors

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.629698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50e35f0d-5d41-424f-9deb-d3ce5da8d8a7 · outbound

This paper cites The affine particle-in-cell method.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation The affine particle-in-cell method

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.742933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1575cfb1-afda-4ed2-88ba-753f4768094e · outbound

This paper cites Grid4D: 4D decomposed hash encoding for high-fidelity dynamic scene rendering.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Grid4D: 4D decomposed hash encoding for high-fidelity dynamic scene rendering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.727252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5998c403-2e73-4922-91df-2c0469bfa33d · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation 3d gaussian splatting for real-time radiance field rendering

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.705106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8e11271b-1c63-4c6d-a053-2d034516022c · outbound

This paper cites Kling ai.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Kling ai

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.679987Z

Source-reported events for the cited work

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Observation 8ae17a44-7ba1-4f05-ab12-cb5d67befba8 · outbound

This paper cites SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.667536Z

Source-reported events for the cited work

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Observation 21789489-6f0d-4114-909a-f587fc61d98c · outbound

This paper cites Pac-nerf: Physics augmented continuum neural ra- diance fields for geometry-agnostic system identification.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Pac-nerf: Physics augmented continuum neural ra- diance fields for geometry-agnostic system identification

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.664040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b3480af6-57dd-4701-83f0-0d7600735429 · outbound

This paper cites Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.679356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4844b72-11f5-4168-96a7-432197965373 · outbound

This paper cites Physgen: Rigid-body physics-grounded image- to-video generation.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Physgen: Rigid-body physics-grounded image- to-video generation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.646190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8f580ad-5fb1-4e08-9e12-57cfce72bc3b · outbound

This paper cites Coxgraph: multi-robot col- laborative, globally consistent, online dense reconstruction system.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Coxgraph: multi-robot col- laborative, globally consistent, online dense reconstruction system

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.629455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ba64e558-9158-446b-9eb1-2ad850079e0c · outbound

This paper cites Raydf: Neural ray-surface distance fields with multi-view consistency.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Raydf: Neural ray-surface distance fields with multi-view consistency

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.606193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2329190a-bd3b-404c-b663-cdef75e97da7 · outbound

This paper cites Ddf-ism: Internal structure modeling of human head using probabilistic directed distance field.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Ddf-ism: Internal structure modeling of human head using probabilistic directed distance field

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.589738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 67b62869-4155-4707-a1ed-9208b9102b99 · outbound

This paper cites Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.731372Z

Source-reported events for the cited work

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Observation aafb8e1b-309b-4265-b5f9-7c35dcf3eb63 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.572160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 43b806d0-7003-420b-b07b-f367c7b20c4a · outbound

This paper cites iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:14:42.065070Z

Source-reported events for the cited work

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Observation ae2ff6de-390e-440a-93b2-2578e8645bf3 · outbound

This paper cites Instant neural graphics primitives with a multires- olution hash encoding.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Instant neural graphics primitives with a multires- olution hash encoding

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.555703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 888c1342-7f0e-463d-90df-0545db073d8f · outbound

This paper cites Mofa-video: Controllable image animation via generative motion field adaptions in frozen image-to-video diffusion model.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Mofa-video: Controllable image animation via generative motion field adaptions in frozen image-to-video diffusion model

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.533858Z

Source-reported events for the cited work

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Observation bca84269-db33-4612-89fb-dbdc3e2a233e · outbound

This paper cites Dreamfusion: Text-to-3d using 2d diffusion.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Dreamfusion: Text-to-3d using 2d diffusion

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.766438Z

Source-reported events for the cited work

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Observation 05dd3248-ed3f-42b8-8512-bdbbec8a2baa · outbound

This paper cites Robocook: Long-horizon elasto-plastic object ma- nipulation with diverse tools.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Robocook: Long-horizon elasto-plastic object ma- nipulation with diverse tools

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.505452Z

Source-reported events for the cited work

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Observation 99dfa758-d315-4e03-a9eb-7a6bcf5cf105 · outbound

This paper cites Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.786810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ef3fca0-8363-46ee-a6f3-b135a7a155ee · outbound

This paper cites Nerfstudio: A modu- lar framework for neural radiance field development.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Nerfstudio: A modu- lar framework for neural radiance field development

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.489879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2ac6adb8-2821-491c-b855-11a9bc818178 · outbound

This paper cites HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.809870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd16d9d7-877b-45b7-8994-5ada93216cbf · outbound

This paper cites ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-12T15:14:42.001425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ccad3dc-23dd-4b62-8432-485b37a2be6c · outbound

This paper cites Raft: Recurrent all-pairs field transforms for optical flow.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Raft: Recurrent all-pairs field transforms for optical flow

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.470513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 051a1cb6-89c0-49ac-9d73-728a4c921bac · outbound

This paper cites NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.827259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea585545-cc44-4dda-b22f-591e470f4f76 · outbound

This paper cites Motionctrl: A unified and flexible motion controller for video generation.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Motionctrl: A unified and flexible motion controller for video generation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.453559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1f6fa08a-ebda-4083-8ee9-3f700389b021 · outbound

This paper cites 4d gaussian splatting for real-time dynamic scene rendering.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation 4d gaussian splatting for real-time dynamic scene rendering

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.437130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4685184c-648e-4e90-b5d1-94558c8ad8e5 · outbound

This paper cites Physgaussian: Physics- integrated 3d gaussians for generative dynamics.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Physgaussian: Physics- integrated 3d gaussians for generative dynamics

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.847243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94526604-e33c-4d11-9c30-30c9f0979ccd · outbound

This paper cites Learning 3d dynamic scene representations for robot manip- ulation.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Learning 3d dynamic scene representations for robot manip- ulation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.407214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ebe8f732-6cd7-47b9-ada7-1b78a5e2df74 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T15:14:41.865350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c99a63f0-d463-4125-9417-9f90f6b06894 · outbound

This paper cites IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation IntrinsicNeRF: Learning Intrinsic Neural Radiance Fields for Editable Novel View Synthesis

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.386817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72c3eb2b-45f8-4082-960d-7d9edee06963 · outbound

This paper cites Diffpano: Scalable and con- sistent text to panorama generation with spherical epipolar- aware diffusion.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Diffpano: Scalable and con- sistent text to panorama generation with spherical epipolar- aware diffusion

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.365964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1097c071-ff02-4a26-ba56-8ea1c96281ee · outbound

This paper cites Feng, Changxi Zheng, Noah Snavely, Jiajun Wu, and William T.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation Feng, Changxi Zheng, Noah Snavely, Jiajun Wu, and William T

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.349192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bae2e573-ffc5-47fc-8ece-c375d7daeefd · outbound

This paper cites The ice cream is slowly melting.

PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation The ice cream is slowly melting

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:14:42.331186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation b69498fd-b26a-48e4-89b7-ef9c5134c419 · inbound

SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding cites this paper.

SeeGround: See and Ground for Zero-Shot Open-Vocabulary 3D Visual Grounding PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T21:29:49.747976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3b704f0-aab2-4e93-86ab-32d1e3515bac · inbound

Zero-Shot 3D Visual Grounding from Vision-Language Models cites this paper.

Zero-Shot 3D Visual Grounding from Vision-Language Models PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T13:12:13.660944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc9fead2-e70e-46c7-a758-bfb90a40f465 · inbound

Physically Viable World Models: A Case for Query-Conditioned Embodied AI cites this paper.

Physically Viable World Models: A Case for Query-Conditioned Embodied AI PhysFlow: Unleashing the Potential of Multi-modal Foundation Models and Video Diffusion for 4D Dynamic Physical Scene Simulation

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-06-29T09:13:16.517222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.