FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases | Paper Citation Record

Observation 967d0ca6-45cb-4c7a-9e19-1358fe15beb2 · outbound

This paper cites Learning optical flow from still images.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Learning optical flow from still images

Reference 1

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Observation e3b27845-89c9-4a5a-bb4c-defab15cf623 · outbound

This paper cites Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Stereo anywhere: Robust zero-shot deep stereo matching even where either stereo or mono fail

Reference 2

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Observation b8b9fadb-8ff3-4bdc-88fe-0a7f7cf7a94c · outbound

This paper cites A framework for the robust estimation of optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases A framework for the robust estimation of optical flow

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:32.427003Z

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Observation 986870ac-230c-41c8-a68b-76d9090ca019 · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:32.585592Z

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Observation 646a0239-4663-4507-933b-4018f029cc02 · outbound

This paper cites Dimensions of motion: Monocular prediction through flow subspaces.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Dimensions of motion: Monocular prediction through flow subspaces

Reference 5

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Observation 07f0bcb9-ce2a-4471-bd71-b3079dfbc866 · outbound

This paper cites High accuracy optical flow estimation based on a theory for warping.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases High accuracy optical flow estimation based on a theory for warping

Reference 6

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Observation 8ebfb160-7b66-405f-9bb3-4c9292740105 · outbound

This paper cites Large displacement optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Large displacement optical flow

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.034263Z

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Observation 6c7f6192-0c69-42c3-8a5a-5eb575840aa8 · outbound

This paper cites A naturalistic open source movie for op- tical flow evaluation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases A naturalistic open source movie for op- tical flow evaluation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.141194Z

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Observation 75bf56fd-1084-4671-b10d-007b10cd93da · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Emerg- ing properties in self-supervised vision transformers

Reference 9

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Observation 2fb9a7b0-5b75-4009-9475-8118a2494b10 · outbound

This paper cites Full flow: Optical flow estimation by global optimization over regular grids.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Full flow: Optical flow estimation by global optimization over regular grids

Reference 10

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Observation c8115d5e-5389-40a0-88a7-49b7ec9a2c6b · outbound

This paper cites Monster: Marry monodepth to stereo unleashes power.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Monster: Marry monodepth to stereo unleashes power

Reference 11

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Observation fda7abb2-9e5b-4ab3-b3e1-e3987f2f47d9 · outbound

This paper cites Flowtrack: Revisiting optical flow for long- range dense tracking.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Flowtrack: Revisiting optical flow for long- range dense tracking

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.437828Z

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Observation ab303125-9002-4b6d-861f-be7965ebf05b · outbound

This paper cites Explicit motion disen- tangling for efficient optical flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Explicit motion disen- tangling for efficient optical flow estimation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.523585Z

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Observation bbd1bf13-33b3-4bdc-87d8-75e604b4e381 · outbound

This paper cites Rethinking opti- cal flow from geometric matching consistent perspective.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Rethinking opti- cal flow from geometric matching consistent perspective

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.580129Z

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Observation 51157368-c5ce-40b3-b157-183e55c8941b · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Flownet: Learning optical flow with convolutional networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.659933Z

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Observation 5e005a9b-5218-487e-9967-892f1e5b4f69 · outbound

This paper cites Fast dynamic radiance fields with time-aware neural voxels.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Fast dynamic radiance fields with time-aware neural voxels

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.757792Z

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Observation d34cabd3-13e9-415a-bb7f-b7d3dd1685c9 · outbound

This paper cites Vision meets robotics: The kitti dataset.The Inter- national Journal of Robotics Research, 32(11):1231–1237,.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Vision meets robotics: The kitti dataset.The Inter- national Journal of Robotics Research, 32(11):1231–1237,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.840863Z

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Observation 6eb92621-8901-4df6-8890-fceeefe817d4 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:33.932607Z

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Observation 4b21276e-df1d-400a-85be-86f1205ea924 · outbound

This paper cites Realflow: Em- based realistic optical flow dataset generation from videos.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Realflow: Em- based realistic optical flow dataset generation from videos

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:34.075944Z

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Observation e9fac067-65df-42f4-8781-d1063e0bd751 · outbound

This paper cites Deep residual learning for image recognition.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Deep residual learning for image recognition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:34.156017Z

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Observation 88c9f056-8270-4225-92ff-dd9062387277 · outbound

This paper cites Subspace methods for recovering rigid motion i: Algorithm and implementation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Subspace methods for recovering rigid motion i: Algorithm and implementation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:34.271250Z

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Observation 3f0bb030-b94e-4068-bd6a-f125cbf15c30 · outbound

This paper cites Determining op- tical flow.Artificial intelligence, 17(1-3):185–203, 1981.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Determining op- tical flow.Artificial intelligence, 17(1-3):185–203, 1981

Reference 22

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Observation 6799c32d-98fa-44cb-bb46-3a145e5cd267 · outbound

This paper cites Efficient coarse-to- fine patchmatch for large displacement optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Efficient coarse-to- fine patchmatch for large displacement optical flow

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.134235Z

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Observation 93aff474-f4e7-420b-af35-851497cc4ef7 · outbound

This paper cites Robust interpola- tion of correspondences for large displacement optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Robust interpola- tion of correspondences for large displacement optical flow

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.119741Z

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Observation 3efc8377-a38f-4e7b-a2de-8332c31617cc · outbound

This paper cites Real-Time Intermediate Flow Estimation for Video Frame Interpolation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:34.662508Z

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Observation 184194de-2e74-4e9f-9b9d-6144bf7b00c9 · outbound

This paper cites Flowformer: A transformer architecture for optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Flowformer: A transformer architecture for optical flow

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.103601Z

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Observation 808c28b6-bffe-44d7-8435-0c63b1e9daa4 · outbound

This paper cites LiteFlowNet3: Resolv- ing Correspondence Ambiguity for More Accurate Optical Flow Estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases LiteFlowNet3: Resolv- ing Correspondence Ambiguity for More Accurate Optical Flow Estimation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.086877Z

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Observation 07fb4930-a909-4949-9c2d-9a58e64db9b9 · outbound

This paper cites Lite- FlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Lite- FlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.071890Z

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Observation 9a005186-90ec-4aca-b216-47aa0637856b · outbound

This paper cites A lightweight optical flow cnn - revisiting data fidelity and reg- ularization.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases A lightweight optical flow cnn - revisiting data fidelity and reg- ularization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.057931Z

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Observation 65c4122b-3475-4691-be3d-d84aef44dd5e · outbound

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FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Unresolved cited work

Reference 30

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Observation 0406e4ab-cb4b-4383-a548-4bc8bc763f4c · outbound

This paper cites Brostow, and Jamie Watson.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Brostow, and Jamie Watson

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.028369Z

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Observation a60219a4-d634-4e69-a6dc-ad8930cc0c93 · outbound

This paper cites Ccmr: High resolution optical flow estimation via coarse-to-fine context-guided motion reasoning.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Ccmr: High resolution optical flow estimation via coarse-to-fine context-guided motion reasoning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:50.012544Z

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Observation d0ecf0f8-d417-47a9-b435-29fcfd3de05b · outbound

This paper cites Ms-raft+: high resolution multi-scale raft.International Journal of Computer Vision, 132(5): 1835–1856, 2024.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Ms-raft+: high resolution multi-scale raft.International Journal of Computer Vision, 132(5): 1835–1856, 2024

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.997000Z

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Observation 9836c31a-d0f7-41d4-a555-ccf895fe8843 · outbound

This paper cites Distractflow: Improving optical flow estimation via real- istic distractions and pseudo-labeling.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Distractflow: Improving optical flow estimation via real- istic distractions and pseudo-labeling

Reference 34

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Observation 2d92c609-2e41-4362-8922-0e56f7ced16a · outbound

This paper cites Defom-stereo: Depth foundation model based stereo matching.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Defom-stereo: Depth foundation model based stereo matching

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.967383Z

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Observation 53bba4de-9415-4b34-aafb-f7afa0e04c58 · outbound

This paper cites Learning to estimate hidden motions with global motion aggregation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Learning to estimate hidden motions with global motion aggregation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.952977Z

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Observation 90af267e-a1d0-4bf9-a244-d331a5a1f219 · outbound

This paper cites Effiscene: Efficient per-pixel rigidity inference for unsupervised joint learning of optical flow, depth, camera pose and motion seg- mentation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Effiscene: Efficient per-pixel rigidity inference for unsupervised joint learning of optical flow, depth, camera pose and motion seg- mentation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.938134Z

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Observation 8bae388e-ea0b-4f13-8ea3-2cb78ea179ae · outbound

This paper cites What mat- ters in unsupervised optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases What mat- ters in unsupervised optical flow

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.922625Z

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Observation b3df536c-dd0c-4b02-803c-8c6127c7d49e · outbound

This paper cites Repurpos- ing diffusion-based image generators for monocular depth estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Repurpos- ing diffusion-based image generators for monocular depth estimation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.907216Z

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Observation 4571ea0e-e960-4233-a4f8-f443de01c346 · outbound

This paper cites The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driv- ing.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases The hci benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driv- ing

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.892037Z

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Observation eb904980-6895-41a4-8c66-d6f8dad65475 · outbound

This paper cites Locally affine sparse-to-dense matching for motion and occlusion estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Locally affine sparse-to-dense matching for motion and occlusion estimation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.876802Z

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Observation a99281de-d4ba-453c-864c-1e6df3ad8050 · outbound

This paper cites Win-Win: Training High-Resolution Vision Transformers from Two Windows.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Win-Win: Training High-Resolution Vision Transformers from Two Windows

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:28:46.484827Z

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Observation 247c2729-0bf9-452e-a346-9503ac11035e · outbound

This paper cites Fast guided global interpolation for depth and motion.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Fast guided global interpolation for depth and motion

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.861222Z

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Observation 4cbcaa54-03e9-4b75-8090-1d9b458c1a7c · outbound

This paper cites Megadepth: Learning single- view depth prediction from internet photos.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Megadepth: Learning single- view depth prediction from internet photos

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.846502Z

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Observation b26b5acb-4b77-43a2-a87c-01b8985ff701 · outbound

This paper cites Playing to vision foundation model’s strengths in stereo match- ing.IEEE Transactions on Intelligent Vehicles, 2024.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Playing to vision foundation model’s strengths in stereo match- ing.IEEE Transactions on Intelligent Vehicles, 2024

Reference 45

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-05T05:28:46.237518Z

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Observation 2f202b15-b1d7-4766-9fa8-d47d5d67cbb6 · outbound

This paper cites Learning by analogy: Reliable supervi- sion from transformations for unsupervised optical flow es- timation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Learning by analogy: Reliable supervi- sion from transformations for unsupervised optical flow es- timation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:37.997573Z

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Observation aebe42f4-3f0a-4ad5-9fc8-23724b350471 · outbound

This paper cites Flow2stereo: Effective self-supervised learning of optical flow and stereo matching.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Flow2stereo: Effective self-supervised learning of optical flow and stereo matching

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.820624Z

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Observation 40869fb2-428a-49a7-b524-2a5e694b34e1 · outbound

This paper cites Unsupervised global and local ho- mography estimation with motion basis learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7885–7899, 2022.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Unsupervised global and local ho- mography estimation with motion basis learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(6):7885–7899, 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.806296Z

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Observation 47e29e3a-7162-4a6f-a531-0d00c1d11f4c · outbound

This paper cites Video frame inter- polation via optical flow estimation with image inpainting.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Video frame inter- polation via optical flow estimation with image inpainting

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.791452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d459ee66-82c1-471e-822d-d4c84ed8f002 · outbound

This paper cites Transflow: Trans- former as flow learner.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Transflow: Trans- former as flow learner

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.777440Z

Source-reported events for the cited work

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Observation 9535b5ca-b1d3-400a-bb8c-631688d36415 · outbound

This paper cites Learning optical flow with kernel patch attention.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Learning optical flow with kernel patch attention

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.763770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d6a3be95-86a5-483c-8c5b-e7e153949ee6 · outbound

This paper cites A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.747133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3b088960-94fa-4daa-b396-926590b2df4c · outbound

This paper cites Spring: A high-resolution high- detail dataset and benchmark for scene flow, optical flow and stereo.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Spring: A high-resolution high- detail dataset and benchmark for scene flow, optical flow and stereo

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.732320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3690aadb-304e-45d7-8392-ccebc9d39c4a · outbound

This paper cites Object scene flow for au- tonomous vehicles.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Object scene flow for au- tonomous vehicles

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.717712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e60c5b8d-d48c-4000-adbd-8107e1de6561 · outbound

This paper cites Recurrent partial kernel network for efficient optical flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Recurrent partial kernel network for efficient optical flow estimation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.703303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 099b6375-3389-4d82-a26d-ad0970e287f5 · outbound

This paper cites Hello GPT-4o, 2024.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Hello GPT-4o, 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.689639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 86bd7cf2-1663-4c32-9e6d-7c4806271708 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Learning transferable visual models from natural language supervi- sion

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:39.724233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71ac322c-154f-4cd8-82c4-3cc45d8b6dd5 · outbound

This paper cites an unresolved cited work.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-05T05:28:49.664096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ecc3a63d-ddb3-4be9-b6fa-cca389e97a33 · outbound

This paper cites Vi- sion transformers for dense prediction.ArXiv preprint, 2021.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Vi- sion transformers for dense prediction.ArXiv preprint, 2021

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.650209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 17ada299-275f-41fb-9b4d-a9c66eebff03 · outbound

This paper cites Optical flow estima- tion using a spatial pyramid network.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Optical flow estima- tion using a spatial pyramid network

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.635758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 56412419-4ab8-4f63-9939-253ba6f5cbd4 · outbound

This paper cites Epicflow: Edge-preserving interpolation of correspondences for optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Epicflow: Edge-preserving interpolation of correspondences for optical flow

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.619991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5b498576-75e9-4433-b8f8-7b71866520b8 · outbound

This paper cites Playing for benchmarks.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Playing for benchmarks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.605715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c604d3d1-1194-4672-b5dd-2a5b42f5821b · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases High-resolution image synthesis with latent diffusion models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:40.762656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7d0b5a0-6e10-4f13-8298-446f9475a59b · outbound

This paper cites Multi-object discov- ery by low-dimensional object motion.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Multi-object discov- ery by low-dimensional object motion

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.578253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c0604be7-725b-462f-9b49-f5066dcbb822 · outbound

This paper cites The surprising effectiveness of diffusion models for optical flow and monocular depth estimation.Advances in Neural Information Processing Systems, 36, 2023.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases The surprising effectiveness of diffusion models for optical flow and monocular depth estimation.Advances in Neural Information Processing Systems, 36, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.562478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 17e0d0d5-4fc7-401c-9d28-7223e2ee88b6 · outbound

This paper cites Videoflow: Exploiting temporal cues for multi-frame optical flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Videoflow: Exploiting temporal cues for multi-frame optical flow estimation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.546614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7bc95b5-c7f5-4ee0-b18b-fd6bf9525c62 · outbound

This paper cites Flowformer++: Masked cost volume autoen- coding for pretraining optical flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Flowformer++: Masked cost volume autoen- coding for pretraining optical flow estimation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.531329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ca2fc311-07f5-417b-97f4-96b481c5d039 · outbound

This paper cites Craft: Cross- attentional flow transformer for robust optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Craft: Cross- attentional flow transformer for robust optical flow

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.514937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 50473326-5b8f-47fe-ab72-32a8bc79e79e · outbound

This paper cites Secrets of optical flow estimation and their principles.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Secrets of optical flow estimation and their principles

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.498852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 40e9eda7-4901-434e-9c07-f1154a1875a6 · outbound

This paper cites Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:41.937568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d628ed86-e348-4a2d-b5ba-1d50df92abe8 · outbound

This paper cites Models matter, so does training: An empirical study of cnns for optical flow estimation.IEEE transactions on pattern analysis and machine intelligence, 42(6):1408–1423, 2019.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Models matter, so does training: An empirical study of cnns for optical flow estimation.IEEE transactions on pattern analysis and machine intelligence, 42(6):1408–1423, 2019

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.470891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cfdf8987-db38-4ba7-afc9-e0d89739f137 · outbound

This paper cites Autoflow: Learning a better training set for optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Autoflow: Learning a better training set for optical flow

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.456147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation bc89be36-e94f-41da-bcc1-25fe9f245158 · outbound

This paper cites Disentan- 11 gling architecture and training for optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Disentan- 11 gling architecture and training for optical flow

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.441882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e71d7011-4cec-4fda-9508-d2142e3a8b8c · outbound

This paper cites Optical flow guided feature: A fast and robust motion representation for video action recognition.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Optical flow guided feature: A fast and robust motion representation for video action recognition

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.426687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 42f77fc3-6f6b-4f72-9a99-69a0edaaa390 · outbound

This paper cites Skflow: Learning optical flow with super kernels.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Skflow: Learning optical flow with super kernels

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.411374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a31c7035-8df3-4f63-8592-cc7b4bffccfb · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Raft: Recurrent all-pairs field transforms for optical flow

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:42.922894Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab4e1769-1483-4f9e-83d4-4408656b6e40 · outbound

This paper cites Displacement-invariant matching cost learning for accurate optical flow estimation.Advances in Neural Information Processing Systems, 33, 2020.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Displacement-invariant matching cost learning for accurate optical flow estimation.Advances in Neural Information Processing Systems, 33, 2020

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.382750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9f8e30df-1463-4fcb-b9aa-3f37602e7be2 · outbound

This paper cites Tracking everything everywhere all at once.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Tracking everything everywhere all at once

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.366180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3ac8a261-3bfd-4f63-b1e2-1f650cefd238 · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Dust3r: Geometric 3d vi- sion made easy

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:43.276766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 975aaac4-d642-4bf3-9ee2-9ee362052b9f · outbound

This paper cites Tartanair: A dataset to push the limits of visual slam.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Tartanair: A dataset to push the limits of visual slam

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.340153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ecb3c14a-5b42-4ea6-89ba-39352907e0fd · outbound

This paper cites Sea-raft: Simple, efficient, accurate raft for optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Sea-raft: Simple, efficient, accurate raft for optical flow

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.324095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 23112d01-df05-418f-812f-932d2180964b · outbound

This paper cites Foundationpose: Unified 6d pose estimation and tracking of novel objects.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Foundationpose: Unified 6d pose estimation and tracking of novel objects

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.305437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5456ddab-8eb1-492d-9f00-3ac514c4934f · outbound

This paper cites Foundationstereo: Zero- shot stereo matching.arXiv, 2025.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Foundationstereo: Zero- shot stereo matching.arXiv, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.290021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 363423bc-35a0-4c69-a6ff-d709850686c0 · outbound

This paper cites Layeredflow: A real-world benchmark for non-lambertian multi-layer optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Layeredflow: A real-world benchmark for non-lambertian multi-layer optical flow

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.275937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 221f2375-45be-4293-9cdb-92ba74702fba · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases 4d gaussian splatting for real-time dynamic scene render- ing

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.260693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e79a9eb6-bc24-4d3f-b7c6-fb7f92ee7d19 · outbound

This paper cites an unresolved cited work.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-05T05:28:49.245388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation edec9b74-2b7f-405e-a884-7156b0684b75 · outbound

This paper cites Gmflow: Learning optical flow via global matching.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Gmflow: Learning optical flow via global matching

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.230848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c979c6e0-05e4-4c1d-9707-7a4e9e161ddb · outbound

This paper cites Unifying flow, stereo and depth estimation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):13941– 13958, 2023.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Unifying flow, stereo and depth estimation.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(11):13941– 13958, 2023

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:49.043364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 71a37c2a-352c-4fdc-a98e-6d621cf2c18d · outbound

This paper cites YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:44.443964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 491552bf-a96d-4734-8ca7-e43f577a2137 · outbound

This paper cites Quadratic video interpolation.Advances in Neural Information Processing Systems, 32, 2019.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Quadratic video interpolation.Advances in Neural Information Processing Systems, 32, 2019

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:48.762862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fac5092d-c0ed-4145-b8cd-99fbcd32cfe1 · outbound

This paper cites V olumetric correspon- dence networks for optical flow.Advances in neural infor- mation processing systems, 32, 2019.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases V olumetric correspon- dence networks for optical flow.Advances in neural infor- mation processing systems, 32, 2019

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:48.535807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 36eaf9a3-fd0e-4a74-921c-465f476a08ae · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Depth anything: Unleashing the power of large-scale unlabeled data

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:48.248902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 87569f14-8bb2-4f49-9a48-d2a0a2d3386f · outbound

This paper cites Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Depth any- thing v2.Advances in Neural Information Processing Sys- tems, 37:21875–21911, 2024

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:48.003240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation de53301e-3be2-473a-8350-d7054e4c1736 · outbound

This paper cites Motion basis learning for unsupervised deep homogra- phy estimation with subspace projection.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Motion basis learning for unsupervised deep homogra- phy estimation with subspace projection

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:47.772739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5abcf0be-c53b-4d66-92e8-180eccafc283 · outbound

This paper cites A du- ality based approach for realtime tv-l 1 optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases A du- ality based approach for realtime tv-l 1 optical flow

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:47.562209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 15e72066-3602-4d64-877b-ce8fd028b833 · outbound

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

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-05T05:28:45.385386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2ec3052-9752-4959-9d38-01f795d5802c · outbound

This paper cites Global matching with overlapping at- tention for optical flow estimation.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Global matching with overlapping at- tention for optical flow estimation

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:47.283616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6ae9656e-6185-47a1-b1bc-9b54d0f6e277 · outbound

This paper cites Dip: Deep inverse patch- match for high-resolution optical flow.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases Dip: Deep inverse patch- match for high-resolution optical flow

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:28:47.068303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5a023086-2bfa-4b9d-83e4-f455d994c04f · outbound

This paper cites C→T→TSKH.

FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases C→T→TSKH

Reference 99

Resolution: malformed identifier; raw_fallback, observed 2026-08-05T05:28:46.803989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers