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Paper Citation Record · LEDGER

Learning an Implicit Physics Model for Image-based Fluid Simulation

As of 23 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2508.08254.

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

pith.paper-citation-record.v1
2508.08254 v1

Coverage vector

measured 58 of 58 reference resolution

Typed states for the displayed outbound observations.

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measured 58 of 58 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

58 of 58 outbound references displayed

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External citation measurements

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Outbound references

Observation a51064d1-25cb-4283-9ca5-e5d5719f899d · outbound

This paper cites VideoPhy: Evaluating Physical Commonsense for Video Generation.

Learning an Implicit Physics Model for Image-based Fluid Simulation VideoPhy: Evaluating Physical Commonsense for Video Generation

Reference 1

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Observation a647483e-c450-4a68-a8b7-829823a50985 · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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Observation 7e31810b-245e-47be-8cdc-86aa375fe307 · outbound

This paper cites Diffdreamer: Towards consistent unsupervised single-view scene extrapolation with conditional diffusion models.

Learning an Implicit Physics Model for Image-based Fluid Simulation Diffdreamer: Towards consistent unsupervised single-view scene extrapolation with conditional diffusion models

Reference 3

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Observation 02b2244e-65f9-48f2-b3dd-6f81d6473b0a · outbound

This paper cites Everybody dance now.

Learning an Implicit Physics Model for Image-based Fluid Simulation Everybody dance now

Reference 4

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Observation c94f16b9-4b15-486e-9a88-e72cdb334516 · outbound

This paper cites Physics informed neural fields for smoke reconstruction with sparse data.

Learning an Implicit Physics Model for Image-based Fluid Simulation Physics informed neural fields for smoke reconstruction with sparse data

Reference 5

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Observation 87543e3d-a197-402b-81c3-db818897deb2 · outbound

This paper cites Salesin, and Richard Szeliski.

Learning an Implicit Physics Model for Image-based Fluid Simulation Salesin, and Richard Szeliski

Reference 6

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Observation 32f97304-4500-4943-9132-7aca2f1d274b · outbound

This paper cites Fluid simulation on neural flow maps.

Learning an Implicit Physics Model for Image-based Fluid Simulation Fluid simulation on neural flow maps

Reference 7

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Observation 69b537ed-9ec4-4597-8f4d-b3d62c926ec9 · outbound

This paper cites Flownet: Learning optical flow with convolutional networks.

Learning an Implicit Physics Model for Image-based Fluid Simulation Flownet: Learning optical flow with convolutional networks

Reference 8

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Observation 1b98f116-98c0-4817-85e9-382132fec12a · outbound

This paper cites Animating Landscape: Self-Supervised Learning of Decoupled Motion and Appearance for Single-Image Video Synthesis.

Learning an Implicit Physics Model for Image-based Fluid Simulation Animating Landscape: Self-Supervised Learning of Decoupled Motion and Appearance for Single-Image Video Synthesis

Reference 9

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Observation 02111779-85aa-4944-89ee-afa5267ca70d · outbound

This paper cites Simulating fluids in real-world still images.

Learning an Implicit Physics Model for Image-based Fluid Simulation Simulating fluids in real-world still images

Reference 10

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Observation 2c6f4b38-4563-407e-aefc-1b692612fe42 · outbound

This paper cites Global transport for fluid reconstruction with learned self- supervision.

Learning an Implicit Physics Model for Image-based Fluid Simulation Global transport for fluid reconstruction with learned self- supervision

Reference 11

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Observation 8024125e-bd68-42e5-acde-86a4519ed314 · outbound

This paper cites Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision.

Learning an Implicit Physics Model for Image-based Fluid Simulation Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision

Reference 12

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Observation 539a717e-7470-415f-87b3-91abeafee056 · outbound

This paper cites Srinivasan, Jonathan T.

Learning an Implicit Physics Model for Image-based Fluid Simulation Srinivasan, Jonathan T

Reference 13

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Observation 7d59c760-8f3f-4fe7-94b2-681bc96039d3 · outbound

This paper cites Modeling the dy- namics of pde systems with physics-constrained deep auto- regressive networks.

Learning an Implicit Physics Model for Image-based Fluid Simulation Modeling the dy- namics of pde systems with physics-constrained deep auto- regressive networks

Reference 14

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Observation c7d77b06-1798-4000-b022-25d52771197e · outbound

This paper cites Neuroanimator: Fast neural network emulation and con- trol of physics-based models.

Learning an Implicit Physics Model for Image-based Fluid Simulation Neuroanimator: Fast neural network emulation and con- trol of physics-based models

Reference 15

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Observation d74cdaa2-2682-4fc7-8f14-85556bb490e9 · outbound

This paper cites Neurofluid: Fluid dynamics grounding with particle- driven neural radiance fields.

Learning an Implicit Physics Model for Image-based Fluid Simulation Neurofluid: Fluid dynamics grounding with particle- driven neural radiance fields

Reference 16

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Observation b9ec9a63-b15f-40c9-8057-160f51a47b19 · outbound

This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

Learning an Implicit Physics Model for Image-based Fluid Simulation Imagen Video: High Definition Video Generation with Diffusion Models

Reference 17

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Observation e3f79b2a-0bca-4887-988c-da44d425b06b · outbound

This paper cites Video dif- fusion models.

Learning an Implicit Physics Model for Image-based Fluid Simulation Video dif- fusion models

Reference 18

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Observation a7561fc1-624a-46eb-b39b-84e1ce42928f · outbound

This paper cites Animating pictures with eulerian mo- tion fields.

Learning an Implicit Physics Model for Image-based Fluid Simulation Animating pictures with eulerian mo- tion fields

Reference 19

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Observation 2c09cc38-b971-4683-a200-8446ee7031dc · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Learning an Implicit Physics Model for Image-based Fluid Simulation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 20

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Observation aca6f1eb-c4f3-41b0-9324-c6dac8810cdf · outbound

This paper cites Animating still land- scape photographs through cloud motion creation.

Learning an Implicit Physics Model for Image-based Fluid Simulation Animating still land- scape photographs through cloud motion creation

Reference 21

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Observation db95c08b-9d3d-4ac2-8445-56fab0536bee · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation 3d gaussian splatting for real-time radiance field rendering

Reference 22

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Observation bdc4813d-272e-46b9-b68d-9b64a3a83ebe · outbound

This paper cites 3d cinemagraphy from a sin- gle image.

Learning an Implicit Physics Model for Image-based Fluid Simulation 3d cinemagraphy from a sin- gle image

Reference 23

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Observation 3407c4ad-fecf-4566-88b5-833f6a8cc7c2 · outbound

This paper cites Graph neural network- accelerated lagrangian fluid simulation.

Learning an Implicit Physics Model for Image-based Fluid Simulation Graph neural network- accelerated lagrangian fluid simulation

Reference 24

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Observation 05b3d75c-705a-4f2c-8609-ad0ac39e22e1 · outbound

This paper cites Wonderland: Navigating 3D Scenes from a Single Image.

Learning an Implicit Physics Model for Image-based Fluid Simulation Wonderland: Navigating 3D Scenes from a Single Image

Reference 25

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Observation 81555280-d935-4e5f-b272-ade68d6b6992 · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation Physgen: Rigid-body physics-grounded image- to-video generation

Reference 26

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Observation 4818f00c-e093-4283-b023-e1bef5b86168 · outbound

This paper cites Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis.

Learning an Implicit Physics Model for Image-based Fluid Simulation Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis

Reference 27

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Observation 60339ebd-3a89-47f1-968b-6d5c38719627 · outbound

This paper cites Controllable animation of fluid elements in still images.

Learning an Implicit Physics Model for Image-based Fluid Simulation Controllable animation of fluid elements in still images

Reference 28

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Observation 29efaa03-6380-418b-a347-81797b61d085 · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 29

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Observation c36cff5e-d7f8-4ad9-94d0-8c95461df76e · outbound

This paper cites Semantic image synthesis with spatially-adaptive nor- malization.

Learning an Implicit Physics Model for Image-based Fluid Simulation Semantic image synthesis with spatially-adaptive nor- malization

Reference 30

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Observation 21d92544-6ee6-4371-a6e6-0048f9d755f9 · outbound

This paper cites Deepmimic: Example-guided deep reinforce- ment learning of physics-based character skills.

Learning an Implicit Physics Model for Image-based Fluid Simulation Deepmimic: Example-guided deep reinforce- ment learning of physics-based character skills

Reference 31

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Source-reported events for the cited work

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Observation 652c0e5d-2aff-4e15-ad5a-0b40aafdeb7c · outbound

This paper cites Raissi, P.

Learning an Implicit Physics Model for Image-based Fluid Simulation Raissi, P

Reference 32

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Source-reported events for the cited work

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Observation b769b4f2-4144-4548-a392-5e46ab659c51 · outbound

This paper cites Vi- sion transformers for dense prediction.

Learning an Implicit Physics Model for Image-based Fluid Simulation Vi- sion transformers for dense prediction

Reference 33

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Source-reported events for the cited work

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Observation ae5a47af-eba6-411a-9bc5-a72a537465e5 · outbound

This paper cites Deep image spatial transformation for person image generation.

Learning an Implicit Physics Model for Image-based Fluid Simulation Deep image spatial transformation for person image generation

Reference 34

Resolution
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Source-reported events for the cited work

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Observation 2bff417a-ca4a-43d8-a290-fc4095ff6cfc · outbound

This paper cites Make-it-4d: Synthesizing a consistent long-term dynamic scene video from a single image.

Learning an Implicit Physics Model for Image-based Fluid Simulation Make-it-4d: Synthesizing a consistent long-term dynamic scene video from a single image

Reference 35

Resolution
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Source-reported events for the cited work

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Observation b83a4b72-b503-41ff-85ee-332eb377440f · outbound

This paper cites 3d photography using context-aware layered depth inpainting.

Learning an Implicit Physics Model for Image-based Fluid Simulation 3d photography using context-aware layered depth inpainting

Reference 36

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 064dcc4f-e67d-42d3-aaf8-e0b228940bb8 · outbound

This paper cites First order motion model for image animation.

Learning an Implicit Physics Model for Image-based Fluid Simulation First order motion model for image animation

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.888553Z digest=sha256:4b326be517a325dafa7de4099dff436ad7b0b9a5080acc667f86485d280fb0fa

Observation aa7a1ef4-b58d-4d9b-84a8-f81d252548f1 · outbound

This paper cites Very deep con- volutional networks for large-scale image recognition.

Learning an Implicit Physics Model for Image-based Fluid Simulation Very deep con- volutional networks for large-scale image recognition

Reference 38

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.891531Z digest=sha256:910f0f98fd4c814b2f5a9cdd6e0667bec5ca7a752166ea45f768537e941545b8

Observation 8fdd602a-a93d-4742-a00c-e50f03291978 · outbound

This paper cites Make-A-Video: Text-to-Video Generation without Text-Video Data.

Learning an Implicit Physics Model for Image-based Fluid Simulation Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 39

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no resolver link, observed 2026-08-15T17:44:17.894749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.894749Z digest=sha256:e4017ad46cfee6304c46f9300bbde9fc370e6a3ee956102b44f39f1f66a0f1e7

Observation 5d0399af-a622-4900-92ae-6677c3cd3ef4 · outbound

This paper cites PhysMotion: Physics-Grounded Dynamics From a Single Image.

Learning an Implicit Physics Model for Image-based Fluid Simulation PhysMotion: Physics-Grounded Dynamics From a Single Image

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T17:44:17.898708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.898708Z digest=sha256:9b8c1d8cac360ac3eab4fffa6b68af2d1ed42f4824f57c44015a96c8b789a1d5

Observation 36451817-a1c5-43d7-a75a-08c16fb61eb1 · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation Raft: Recurrent all-pairs field transforms for optical flow

Reference 41

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unresolved
no resolver link, observed 2026-08-15T17:44:17.902298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.902298Z digest=sha256:859cb65facdb432029c14a8e7850063493a015b61c2b23f03874d2450e122352

Observation 156cc7cf-637d-41f5-a7bd-6668f5c56b98 · outbound

This paper cites Accelerating eulerian fluid simulation with convolutional networks.

Learning an Implicit Physics Model for Image-based Fluid Simulation Accelerating eulerian fluid simulation with convolutional networks

Reference 42

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.905641Z digest=sha256:aadf04b2ea52cbb78f7394692363091c38be05bd1be90b19095f3c4fe2f94e69

Observation 1180c8e4-a113-424a-80c4-08c8c3053a91 · outbound

This paper cites Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize.

Learning an Implicit Physics Model for Image-based Fluid Simulation Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize

Reference 43

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.909013Z digest=sha256:df9465dfb72d55e0e2f01117238ca75a3e96cd37e9eafc9d11fecf936590c2e2

Observation bd1f8dc8-a73a-4657-9fb4-4fa7596116f7 · outbound

This paper cites Teaching the incompressible navier–stokes equations to fast neural surrogate models in three dimensions.

Learning an Implicit Physics Model for Image-based Fluid Simulation Teaching the incompressible navier–stokes equations to fast neural surrogate models in three dimensions

Reference 44

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.912497Z digest=sha256:00562f9d63d51dc33c19ec57cdccbfcc990265962f93d7d91297a9efe7665fd4

Observation 49bab27e-8684-430f-87ef-763cabb93520 · outbound

This paper cites SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency.

Learning an Implicit Physics Model for Image-based Fluid Simulation SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency

Reference 45

Resolution
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no resolver link, observed 2026-08-15T17:44:17.916049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.916049Z digest=sha256:74540f8e792b50095d83c6b8537b7d65836bd1154f377dcce8207e6ebf03fa7e

Observation fd214686-c2ae-48df-a574-2965e6e91c64 · outbound

This paper cites Mo- tion dreamer: Realizing physically coherent video genera- tion through scene-aware motion reasoning.

Learning an Implicit Physics Model for Image-based Fluid Simulation Mo- tion dreamer: Realizing physically coherent video genera- tion through scene-aware motion reasoning

Reference 46

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unresolved
no resolver link, observed 2026-08-15T17:44:17.919638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.919638Z digest=sha256:4b55a14165c385bc0f1ca60a37310820848069bb30c48ed1557dc9d2f90e87e4

Observation 16eba83b-8983-48c5-8641-d96c7ced76d9 · outbound

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

Learning an Implicit Physics Model for Image-based Fluid Simulation CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-15T17:44:17.922943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:44:17.922943Z digest=sha256:2cefdde4417de68ea8ab5237e18dfa113f6547920d7cdcfdc90c284d7a717918

Observation bc50c30f-e056-4180-aff3-527daeefb92f · outbound

This paper cites Inferring hybrid neural fluid fields from videos.

Learning an Implicit Physics Model for Image-based Fluid Simulation Inferring hybrid neural fluid fields from videos

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.279434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.926698Z digest=sha256:ab5e4a5514b5a36db6d6e77f7fd77c8381d63999d9f9430e033022f835c72cda

Observation d16dff4f-576d-49c9-b8b0-edb54cba09bb · outbound

This paper cites Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, and Charles Her- rmann.

Learning an Implicit Physics Model for Image-based Fluid Simulation Freeman, Forrester Cole, Deqing Sun, Noah Snavely, Jiajun Wu, and Charles Her- rmann

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.269037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.930271Z digest=sha256:dffda39d5a858fba52f93a0a9b1f9d739534d6ea8bc886a03433777cee599f8b

Observation 4ae87b91-8c1e-40f8-87dc-728897f32f85 · outbound

This paper cites ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis.

Learning an Implicit Physics Model for Image-based Fluid Simulation ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a95f4742-a76f-4bf9-b544-76581d140ace · outbound

This paper cites Tomofluid: Reconstructing dynamic fluid from sparse view videos.

Learning an Implicit Physics Model for Image-based Fluid Simulation Tomofluid: Reconstructing dynamic fluid from sparse view videos

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.258308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation c873e5cd-800a-4053-8df6-56d592f355ba · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Learning an Implicit Physics Model for Image-based Fluid Simulation The unreasonable effectiveness of deep features as a perceptual metric

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.248029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.940810Z digest=sha256:01b039dbab63a9701c2abb9279de62450ff8b5fcbca5cdfe0a8aee0839d39997

Observation a7565275-7819-4ad8-8ffd-ea5376093a85 · outbound

This paper cites Physdreamer: Physics-based interac- tion with 3d objects via video generation.

Learning an Implicit Physics Model for Image-based Fluid Simulation Physdreamer: Physics-based interac- tion with 3d objects via video generation

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.236525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.944503Z digest=sha256:8afb62eb792eaeec3c9d3265c2471a311629ed3a68fa591e1ac9e3601960ff4c

Observation d626c111-a95b-4c0a-8773-0fd5593ef7d0 · outbound

This paper cites Both stages use the Adam optimizer with a 1e-4 learning rate and (0, 0.9) for betas.

Learning an Implicit Physics Model for Image-based Fluid Simulation Both stages use the Adam optimizer with a 1e-4 learning rate and (0, 0.9) for betas

Reference 54

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verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.224936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.947790Z digest=sha256:d2ba3b41ff2ac6be61a697435201d13e6985620f51da17f49d74bfb8a40e72f0

Observation 142fdede-9b45-4788-b8b2-a2ebb73cb179 · outbound

This paper cites an unresolved cited work.

Learning an Implicit Physics Model for Image-based Fluid Simulation Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-15T17:44:18.214141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.951362Z digest=sha256:97bb55d672adc01c22c22cb74e392adfbe0099d60c94af464adda2aa16a4e496

Observation a2625922-c8bc-4c79-951e-4cfcee2e6370 · outbound

This paper cites The animation part contains an inpainter to inpaint each LDI, an encoder to extract features for each LDI, and a decoder to decode the feature maps rendered by 3D Gaussians.

Learning an Implicit Physics Model for Image-based Fluid Simulation The animation part contains an inpainter to inpaint each LDI, an encoder to extract features for each LDI, and a decoder to decode the feature maps rendered by 3D Gaussians

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.203727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.955159Z digest=sha256:ce9a0803f89b9c1c8c8a3634416a9242c2fb426c5ae93962218639829163a60b

Observation be77a8ea-3df8-4125-967b-2d3099f7cc33 · outbound

This paper cites Video Results We include video results generated by our methods and baseline methods in the supplementary materials.

Learning an Implicit Physics Model for Image-based Fluid Simulation Video Results We include video results generated by our methods and baseline methods in the supplementary materials

Reference 57

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malformed identifier
raw_fallback, observed 2026-08-15T17:44:18.192613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.959162Z digest=sha256:0e1614e71ddef80fc6c09f89e59efc69d106410b7501de73d71fe9f7769b2db5

Observation 520a5138-3f90-4eec-8a00-9e3bfc320bf4 · outbound

This paper cites In particular, the lack of pressure fields limits its ability to capture interactions such as river merging.

Learning an Implicit Physics Model for Image-based Fluid Simulation In particular, the lack of pressure fields limits its ability to capture interactions such as river merging

Reference 58

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verified fuzzy
raw_fallback, observed 2026-08-15T17:44:18.181044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T17:44:17.962956Z digest=sha256:6ae5713f0750c1c36bd2ec464762c09f65f4ac2392b2ea294953984588bc5a23

Pith citing papers

No inbound Pith citation observations are available.