Paper Citation Record · LEDGER

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors

As of 12 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2411.17249.

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

pith.paper-citation-record.v1
2411.17249 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:26:49.610580Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-13T00:56:18.867382Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

64 of 64 outbound references displayed

verified exact0
verified fuzzy27
unresolved37
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 187910a2-3575-461a-9c28-bc9b1ac1baef · outbound

This paper cites Stable diffusion version 2.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Stable diffusion version 2

Reference 1

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Observation 59d925b9-d6a3-43cc-b7ef-504d0728e725 · outbound

This paper cites Rethinking induc- tive biases for surface normal estimation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Rethinking induc- tive biases for surface normal estimation

Reference 2

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Observation e4575005-7e21-49cc-9ca6-4366e1e6a895 · outbound

This paper cites Es- timating and exploiting the aleatoric uncertainty in surface normal estimation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Es- timating and exploiting the aleatoric uncertainty in surface normal estimation

Reference 3

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Observation 262d1cd3-b732-4e78-8a39-4f313c02ed5d · outbound

This paper cites Marr revisited: 2d-3d alignment via surface normal prediction.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Marr revisited: 2d-3d alignment via surface normal prediction

Reference 4

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Observation 2518f65c-8653-4fde-acf1-c5c1fb3e8f37 · outbound

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

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 5

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Observation 7e247130-d8f9-43b0-9ce8-11808caffa17 · outbound

This paper cites Video generation models as world simulators.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Video generation models as world simulators

Reference 6

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Observation 65ececb4-b366-4fa1-bc0b-fbd7eea0694e · outbound

This paper cites A naturalistic open source movie for opti- cal flow evaluation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors A naturalistic open source movie for opti- cal flow evaluation

Reference 7

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Observation 245df49e-2c83-4604-98f2-8341d16205c1 · outbound

This paper cites A computational approach to edge detection.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors A computational approach to edge detection

Reference 8

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This paper cites Learning structure affinity for video depth estima- tion.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Learning structure affinity for video depth estima- tion

Reference 9

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Observation 56c2cd09-9da3-4dc2-b004-a6773999b77c · outbound

This paper cites Magicpose: Realistic human poses and facial expressions retargeting with identity-aware diffusion,.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Magicpose: Realistic human poses and facial expressions retargeting with identity-aware diffusion,

Reference 10

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Observation 01b72b87-6db6-4e18-b6fc-9db078e79c6c · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 11

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Observation 97050a54-893b-4653-b268-dfea066a0245 · outbound

This paper cites Surface normal estimation of tilted images via spatial rectifier.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Surface normal estimation of tilted images via spatial rectifier

Reference 12

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Observation 658f4f7f-8352-4d5b-8e2e-38a96f3f628e · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors An image is worth 16x16 words: Transformers for image recognition at scale

Reference 13

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This paper cites Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Omnidata: A scalable pipeline for making multi- task mid-level vision datasets from 3d scans

Reference 14

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Observation 6ec9e73d-34da-4b78-aaa4-2128f5f59dc9 · outbound

This paper cites Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Predicting depth, surface nor- mals and semantic labels with a common multi-scale con- volutional architecture

Reference 15

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This paper cites Gpt-3: Its nature, scope, limits, and consequences.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Gpt-3: Its nature, scope, limits, and consequences

Reference 16

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Observation 032f69e2-a540-41a1-8fa4-26a649dd0315 · outbound

This paper cites Unfolding an indoor origami world.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Unfolding an indoor origami world

Reference 17

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Observation 82fae043-fe50-4ef7-99ee-7871cdf01673 · outbound

This paper cites Deep ordinal regression net- work for monocular depth estimation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Deep ordinal regression net- work for monocular depth estimation

Reference 18

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Observation 2ae88873-2835-4af3-abb9-d76d0f0c1ef9 · outbound

This paper cites Geowiz- ard: Unleashing the diffusion priors for 3d geometry esti- mation from a single image.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Geowiz- ard: Unleashing the diffusion priors for 3d geometry esti- mation from a single image

Reference 19

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This paper cites Fine-tuning image-conditional diffusion models is easier than you think.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Fine-tuning image-conditional diffusion models is easier than you think

Reference 20

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Vision meets robotics: The kitti dataset

Reference 21

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This paper cites Unsupervised monocular depth estimation with left- right consistency.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Unsupervised monocular depth estimation with left- right consistency

Reference 22

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Observation e8dbd863-3354-471b-b356-c0b7b7935331 · outbound

This paper cites Sparsectrl: Adding sparse controls to text-to-video diffusion models.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Sparsectrl: Adding sparse controls to text-to-video diffusion models

Reference 23

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Observation 975036b5-c9bd-48f2-964e-05b94accd2ff · outbound

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

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 24

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Cameractrl: Enabling camera control for text-to-video generation, 2024

Reference 25

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This paper cites Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

Reference 26

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Au- tomatic photo pop-up

Reference 27

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Recov- ering surface layout from an image

Reference 28

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This paper cites Animate anyone: Consistent and controllable image-to-video synthesis for character animation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Animate anyone: Consistent and controllable image-to-video synthesis for character animation

Reference 29

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Reference 30

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors 3d common corruptions and data augmentation

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Repurpos- ing diffusion-based image generators for monocular depth estimation

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Wetzstein

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Sift flow: Dense correspondence across different scenes

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Refusion: 3d reconstruction in dynamic environments for rgb-d cameras exploiting resid- uals

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors State of the art on diffusion models for visual computing

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer

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Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Learning Temporally Consistent Video Depth from Video Diffusion Priors

Reference 45

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Reference 50

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Reference 51

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This paper cites Less is more: Consistent video depth estimation with masked frames modeling.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Less is more: Consistent video depth estimation with masked frames modeling

Reference 52

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Reference 53

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Observation b0acf96b-5ada-4329-8bae-d08283b2dda1 · outbound

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

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T12:26:49.571053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d05fd471-d3b3-4e86-bdd8-70c6a9869666 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Depth anything: Unleashing the power of large-scale unlabeled data

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:50.049053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 117cd4ed-22cd-4fbd-b815-4464841292b2 · outbound

This paper cites Depth Anything V2.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Depth Anything V2

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-12T12:26:49.579883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a1668ba-09a1-4b4b-8c4b-55c34ccf21cb · outbound

This paper cites En- forcing geometric constraints of virtual normal for depth pre- diction.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors En- forcing geometric constraints of virtual normal for depth pre- diction

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:50.034622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d5764505-c86a-48da-9dd9-939a82876222 · outbound

This paper cites Rgb ↔x: Image decomposition and synthesis using material-and lighting-aware diffusion models.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Rgb ↔x: Image decomposition and synthesis using material-and lighting-aware diffusion models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:50.018555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2acbd3f7-9950-4622-82d2-f444f9d6f573 · outbound

This paper cites Hierarchical normalization for robust monocular depth estimation.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Hierarchical normalization for robust monocular depth estimation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:50.002051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c1eebdc-368c-4d59-b42d-321006368869 · outbound

This paper cites Arf: Artistic radiance fields.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Arf: Artistic radiance fields

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:49.986336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2a277178-70c8-4b7b-9787-2d804b36647f · outbound

This paper cites an unresolved cited work.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:26:49.970703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d80dda8-fece-4ef0-aa11-14c1ef3d8e8e · outbound

This paper cites We utilize the official implementations of Depth Anything V2 [56] and Marigold-E2E-FT [20], adapting temporal blocks from the UnetMotion architecture in the Diffusers [49] library.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors We utilize the official implementations of Depth Anything V2 [56] and Marigold-E2E-FT [20], adapting temporal blocks from the UnetMotion architecture in the Diffusers [49] library

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:26:49.955597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c3225c1-1da2-49aa-916e-77646c08e3d5 · outbound

This paper cites an unresolved cited work.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-12T12:26:49.938514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 55c7ddf7-4846-4016-ab8f-eb0e4350a923 · outbound

This paper cites an unresolved cited work.

Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors Unresolved cited work

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T12:26:49.368602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation c46a1f6e-6f13-4b8f-9a6c-98450bc7130f · inbound

Video Generation Models are General-Purpose Vision Learners cites this paper.

Video Generation Models are General-Purpose Vision Learners Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-13T00:56:18.867382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.