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Paper Citation Record · LEDGER

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training

As of 18 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2412.06029.

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

pith.paper-citation-record.v1
2412.06029 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:09:26.217767Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:31:56.614294Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:28:15.841296Z

Reference resolution

55 of 55 outbound references displayed

  • verified exact1
  • verified fuzzy24
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 812eee19-68e4-4feb-8a99-6071cc6a5fe4 · outbound

This paper cites Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation

Reference 1

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source=pdf_text observed=2026-08-11T20:09:26.059474Z digest=sha256:edbd7026e67c29548f487631a97f7b556077a1e83e4e944f89c39c33388b2245

Observation dd749c36-164b-4e96-9c29-5e6d88b7bfe9 · outbound

This paper cites On Inductive Biases That Enable Generalization of Diffusion Transformers.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training On Inductive Biases That Enable Generalization of Diffusion Transformers

Reference 2

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local_arxiv, observed 2026-08-11T20:09:26.359815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a608900b-f220-4e3f-827e-e57222279721 · outbound

This paper cites Bring Metric Functions into Diffusion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Bring Metric Functions into Diffusion Models

Reference 3

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Observation 048624cc-8ad7-406f-9e6d-7029b903d33e · outbound

This paper cites Align your latents: High-Resolution Video Synthesis with Latent Diffusion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Align your latents: High-Resolution Video Synthesis with Latent Diffusion Models

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.069636Z digest=sha256:7a9485bd79848b16253d34b610c0bf37ae5c96e738aeb8476a82b4f414486459

Observation 134d1a11-ff1d-4aad-ab6d-d9e231fba72f · outbound

This paper cites VideoCrafter1: Open Diffusion Models for High-Quality Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 5

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source=pdf_text observed=2026-08-11T20:09:26.072386Z digest=sha256:dc1e3e823d9270add4370ddd7db669518ede81527329d3c688079aea0ac06dab

Observation ba12f3e2-9e05-44c5-9b51-004e8cca5fd4 · outbound

This paper cites VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models

Reference 6

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Observation 3f352b9e-2658-44f9-b929-f5ef04db577b · outbound

This paper cites Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Instantsplat: Unbounded sparse-view pose-free gaus- sian splatting in 40 seconds, 2024

Reference 7

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source=pdf_text observed=2026-08-11T20:09:26.079596Z digest=sha256:760136ab253b731e329e062ed73f884efacb67c935f15cdcbf12a2fa16a0f31d

Observation 95218c3e-97bf-4226-b960-3279becab947 · outbound

This paper cites Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models

Reference 8

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Observation 60684632-ef57-4819-be1d-ab91ec906ca3 · outbound

This paper cites Animatediff: Animate your personalized text-to- image diffusion models without specific tuning.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Animatediff: Animate your personalized text-to- image diffusion models without specific tuning

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f43224c7-286e-4da2-b8c0-9fff15ca40f4 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to- Image Diffusion Models without Specific Tuning.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training AnimateDiff: Animate Your Personalized Text-to- Image Diffusion Models without Specific Tuning

Reference 10

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 21b7cc7e-85d0-44f0-9ed7-e5e111cb3b73 · outbound

This paper cites CameraCtrl: Enabling Camera Control for Text-to-Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training CameraCtrl: Enabling Camera Control for Text-to-Video Generation

Reference 11

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Observation fbde206d-4b82-434c-97bd-2d197a97d0fa · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilib- rium.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a5881420-f2fe-4079-b2e5-33f74b80ea54 · outbound

This paper cites Denoising dif- fusion probabilistic models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Denoising dif- fusion probabilistic models

Reference 13

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Observation b3974b29-d660-4819-bdba-ed28a7cf8762 · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Imagen Video: High Definition Video Generation with Diffusion Models

Reference 14

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Observation 555a93fc-fbeb-457d-8a6d-dfec23ab2704 · outbound

This paper cites Video Diffu- sion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Video Diffu- sion Models

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.104192Z digest=sha256:eb048b5dcce80f7460a36ad84a6d96706734131e7cdb4b53fe5d4a769daf65e7

Observation 225a167a-0256-453c-80e2-1ce6740ac057 · outbound

This paper cites Training-free Camera Control for Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Training-free Camera Control for Video Generation

Reference 16

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Observation 0e2c8c91-5007-4f00-9b6e-c3c4bd05b59d · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training LoRA: Low-Rank Adaptation of Large Language Models

Reference 17

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Observation 5cc081ed-7fa9-453c-a603-b87878b6f061 · outbound

This paper cites Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser

Reference 18

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Observation cc0c6c35-d0c3-4433-95c5-e4f45322ce43 · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training 3d gaussian splatting for real-time radiance field rendering

Reference 19

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Observation 5a61912f-53df-4acf-bb45-68fdeff89234 · outbound

This paper cites Fifo-diffusion: Generating infinite videos from text without training.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Fifo-diffusion: Generating infinite videos from text without training

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation d0c2f7c2-8f9d-4a9a-b5c9-752b71b894f2 · outbound

This paper cites Ground- ing image matching in 3d with mast3r, 2024.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Ground- ing image matching in 3d with mast3r, 2024

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eefe9455-2d78-4554-af60-1c3f56fea41b · outbound

This paper cites Re- conx: Reconstruct any scene from sparse views with video diffusion model, 2024.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Re- conx: Reconstruct any scene from sparse views with video diffusion model, 2024

Reference 22

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.122615Z digest=sha256:4ab202424235cb2993ad2ff8031a8fa0bf129cefc7bf8de03fdc6acc2ac9bb69

Observation e3e75ca5-70ce-4f5b-a42c-68eddb1187c0 · outbound

This paper cites Repaint: Inpainting using denoising diffusion probabilistic models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Repaint: Inpainting using denoising diffusion probabilistic models

Reference 23

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.124480Z digest=sha256:36ac9b3522894f399dce63b16762150b8161dd191d4f92037e1f8201784e9a38

Observation cb6b4f51-dc86-41c7-8115-f263766fe271 · outbound

This paper cites Srinivasan, Matthew Tancik, Jonathan T.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Srinivasan, Matthew Tancik, Jonathan T

Reference 24

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source=pdf_text observed=2026-08-11T20:09:26.127144Z digest=sha256:d4808ec53642475a1ede4de9f514e6deffc2bf9652d6083e7b55230649459904

Observation 92a1db0b-14e9-4e8b-9e67-735d6f378de5 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Improved denoising diffusion probabilistic models

Reference 25

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source=pdf_text observed=2026-08-11T20:09:26.129755Z digest=sha256:18fa36a46fbad85559dce5ac504d892156299d5b323917e59758519b173d526e

Observation 6cb675ce-d5fe-4ee3-8260-719895cedfa1 · outbound

This paper cites Scalable diffusion models with transformers.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Scalable diffusion models with transformers

Reference 26

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source=pdf_text observed=2026-08-11T20:09:26.131900Z digest=sha256:8eb7b5692c4e3330ce474ffc1a8ba98ea1bf863a8ac25457ba212ecb080bf91b

Observation f72b536e-3171-4599-ab0e-b9d4c29d858c · outbound

This paper cites Accelerating 3D Deep Learning with PyTorch3D.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Accelerating 3D Deep Learning with PyTorch3D

Reference 27

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source=pdf_text observed=2026-08-11T20:09:26.134277Z digest=sha256:c599328c6ad5186f5c53f6982efb9273110ce2732cd73a84811c0e0ad6a0e8c9

Observation 0aeb3646-7e2c-4b3e-aee5-70cf8e95e082 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training U-net: Convolutional networks for biomedical image segmentation

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation eb6aaded-c9a7-4cea-bcb3-a65e66330b43 · outbound

This paper cites Dragdiffusion: Harnessing diffusion models for interactive point-based image editing.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 24d1f096-4339-479d-acef-ab702819c9b9 · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 30

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 53a27d15-ca11-41a4-8eae-72eabb4ef455 · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs

Reference 31

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source=pdf_text observed=2026-08-11T20:09:26.146544Z digest=sha256:1376a995dfdb43b2ebf651112088ddee9e152e839d71e21f06abce098a9d4e34

Observation 2ec0f422-616f-4b5b-91e8-9ccd9631c7e3 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Deep unsupervised learning using nonequilibrium thermodynamics

Reference 32

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source=pdf_text observed=2026-08-11T20:09:26.149774Z digest=sha256:429b489eca398089c8804be8185cb81970b6b58ab6efd0d001e02b7e05e4b7cf

Observation 00b0058a-7bf1-482e-9680-622cf9348f5a · outbound

This paper cites Denoising Diffusion Implicit Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Denoising Diffusion Implicit Models

Reference 33

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source=pdf_text observed=2026-08-11T20:09:26.152603Z digest=sha256:da98946b645d80782f6132324008da93da2370ffad4f470c4edb8c23c03cff25

Observation 152fe9ef-0338-4c97-b18b-b022e92f040c · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Score-Based Generative Modeling through Stochastic Differential Equations

Reference 34

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source=pdf_text observed=2026-08-11T20:09:26.155309Z digest=sha256:cffab3b3af350baea7f3fbf7626f21b56ed5b4a75531f900d243a89a51e1f201

Observation ec924fe2-73c1-4ef0-bc97-34fd4b38899d · outbound

This paper cites Towards Accurate Generative Models of Video: A New Metric & Challenges.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 35

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source=pdf_text observed=2026-08-11T20:09:26.158396Z digest=sha256:7d2d14c7799e1346768f7129c4250cd150fd82feb86965547eb4af64a0fd37d9

Observation 18e8b20e-f38d-473a-9b96-31a6a1a44cca · outbound

This paper cites Phenaki: Variable Length Video Generation from Open Do- main Textual Description.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Phenaki: Variable Length Video Generation from Open Do- main Textual Description

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.466062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.162019Z digest=sha256:2f1af037fb3250d57bc1f4d20e5d1c4a8c0eef21a1570d0ab89c8591d6843d15

Observation 356554c1-71d8-44d6-b411-2678b8681ca5 · outbound

This paper cites MCVD-Masked Conditional Video Diffusion for Pre- diction, Generation, and Interpolation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training MCVD-Masked Conditional Video Diffusion for Pre- diction, Generation, and Interpolation

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.457433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.164841Z digest=sha256:c53fe9e4e068750d13c63d06ff1ecac4e177480d5a53ff2921687957796e3dab

Observation 0a8dfdf0-cba8-4dfc-9a98-6f7f74f4c223 · outbound

This paper cites Dust3r: Geometric 3d vi- sion made easy.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Dust3r: Geometric 3d vi- sion made easy

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.448953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.167720Z digest=sha256:a7383d80930230ee622d685a88f667d6f5d153fb097f9a39aa6f3b336132191c

Observation 939a4f95-f6ed-424b-abd9-082d3cf96539 · outbound

This paper cites VideoComposer: Compositional Video Synthesis with Motion Controllability.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training VideoComposer: Compositional Video Synthesis with Motion Controllability

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.441108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.170951Z digest=sha256:6b8e6ab499aff3846423b2995cae0418b7fedccea72fb2595a4f5f67b07348a2

Observation 550446b2-3e6b-4da4-ac66-8e473776fc9e · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Motionctrl: A unified and flexible motion controller for video generation

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.432430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.173336Z digest=sha256:72813c48574ec708f6c03aec3cea49d8dde6be3efac67d7d26b6f819a6e289e7

Observation 85e2739c-649f-432c-b1fa-744cd63f0157 · outbound

This paper cites Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.425544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.175702Z digest=sha256:745f26c3e816e822f67ee62f2b54fbf37d9e44dcbbd9eb89d47ca7f7ed5ecbc9

Observation 2590fe22-3bae-4736-85ee-4e428214c23b · outbound

This paper cites CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.178025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.178025Z digest=sha256:270a7c1a85193dce788ce76a187a8573ca6c419ff1822f816c157435d8b884a6

Observation bd40c098-42b4-4013-b9ff-cb5d317d51ac · outbound

This paper cites Direct-a-video: Customized video generation with user- directed camera movement and object motion.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Direct-a-video: Customized video generation with user- directed camera movement and object motion

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.418277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.181298Z digest=sha256:06bc188a1ee79e00309cf75e8ed4df64acbfaecbdf8bcb40ebd96c612a7601af

Observation 55f58ed6-41fb-4c12-8da5-378d14c6cd3d · outbound

This paper cites No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.184389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.184389Z digest=sha256:a17bac9f8039be761bed3174992a2bbf29c5e4da5af9ced70528729405743786

Observation e83e1f48-76e9-4652-b2af-b5e015e576f6 · outbound

This paper cites DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.187222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.187222Z digest=sha256:d2a75fa0b298833e71f0f103282938a9a761a1e48d2b6bdd1990e7136d148469

Observation d410265e-3b9e-4337-b12e-401add4067fe · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.190032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.190032Z digest=sha256:126a374d1cae5c7443ee788bbd20cda748964ca04895f18a0e16d5a312203857

Observation d1130f18-a86c-48ce-9ced-e628cbf40f9f · outbound

This paper cites ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.192738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.192738Z digest=sha256:ec799bf96fa9a3926d84d1bad72d03aba727e3f57fa7e88d94c2b3bc1ac78249

Observation 26e1618f-0236-45a3-9ae0-79b1fb0bda40 · outbound

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

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.195788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.195788Z digest=sha256:a67169d607912db9be3102b7e17bb09a1df9a4767a14e150247133384a7ad4e4

Observation ca5addaa-c94e-4e1c-b84f-e915a73c9fc8 · outbound

This paper cites GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.198399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.198399Z digest=sha256:879bc21f2cf94e19ea0dad2c891441a9c902d664f4ee4b8d4f425b5205a43a12

Observation 495511b9-182f-4105-a368-40eb1a8cbb5f · outbound

This paper cites Stereo Magnification: Learning View Synthesis using Multiplane Images.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Stereo Magnification: Learning View Synthesis using Multiplane Images

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-11T20:09:26.201718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T20:09:26.201718Z digest=sha256:72105abcf1bfa6573eff050fced9e7657c66083558f4a18b699206acce27cbaf

Observation 184b73bf-6bbb-4cf3-9afe-5cefe9fc8627 · outbound

This paper cites Denoising is conducted using the video diffusion model until reaching the predetermined latent reframing step, which is step 8.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training Denoising is conducted using the video diffusion model until reaching the predetermined latent reframing step, which is step 8

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.409363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.205649Z digest=sha256:1d4c0ba6f9922ee76a3ce08634e83350b58ed0e3ae8fb7614c0d4b0ee8277d43

Observation 816b8473-d6a2-42f7-a70a-1ff99592800e · outbound

This paper cites 3.2, is then applied to generate the reframed video at the target camera pose.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training 3.2, is then applied to generate the reframed video at the target camera pose

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.400492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.208723Z digest=sha256:14c6d2811a0cda1d7282ddbce9c1a16a5931c64900f2a358c7364ff240f080d8

Observation 1c7457cd-8c84-4135-931d-9674c5488aae · outbound

This paper cites As outlined in Sec.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training As outlined in Sec

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.391325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.211115Z digest=sha256:b87cece4985f70905c0d892b5797f39c51f7820aa94b148e6fb9d9a5b22c46e1

Observation 33654b29-9b04-4df5-8b32-2184c0a07842 · outbound

This paper cites At this stage, the input to the denoising network combines un- known and known regions, as described in Eq.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training At this stage, the input to the denoising network combines un- known and known regions, as described in Eq

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.383889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.214345Z digest=sha256:c9e5957fb22920d49463ba3b95fe659f396c81f9dfae8aad69573d9613236308

Observation 1609c16c-0273-4805-aabb-216b985fda7c · outbound

This paper cites After this, the known region is no longer merged.

Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training After this, the known region is no longer merged

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T20:09:26.375955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-11T20:09:26.217767Z digest=sha256:2f74cdf2a7f27e0e5c16d7570b1672270a6986aab08b2c6d49f6010fa0211f50

Pith citing papers

Observation 822936e3-7891-4abc-93ed-32d1f31dd543 · inbound

Towards Understanding Camera Motions in Any Video cites this paper.

Towards Understanding Camera Motions in Any Video Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-16T11:31:56.614294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:31:56.614294Z digest=sha256:445cad039e9897da7d750e06c42bca4090599b0d479e5c87cfb3a1c2067785d7

Observation caa02212-344e-4660-858c-66f50bb2be53 · inbound

EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance cites this paper.

EPiC: Efficient Video Camera Control Learning with Precise Anchor-Video Guidance Latent-Reframe: Enabling Camera Control for Video Diffusion Model without Training

Reference 86

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:28:15.897713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T13:28:15.758987Z digest=sha256:abfe69a794935b037847316ad97a8a8436eb0628aa3d636c57d16d8cee61787e