Paper Citation Record · LEDGER

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models

As of 22 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2504.14535.

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

pith.paper-citation-record.v1
2504.14535 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:49:08.680321Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

verified exact1
verified fuzzy23
unresolved15
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation afdfdc04-9960-4a07-821a-e0333e59b222 · outbound

This paper cites Video Diffusion Models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Video Diffusion Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.505743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e39bc3f7-676a-4b87-9b79-01b9bff50a33 · outbound

This paper cites Align your latents: High-resolution video syn- thesis with latent diffusion models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Align your latents: High-resolution video syn- thesis with latent diffusion models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.277884Z

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 2cceb0d4-f9c2-4518-ba03-1530e156047b · outbound

This paper cites Hierarchical patch diffusion models for high-resolution video generation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Hierarchical patch diffusion models for high-resolution video generation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.262788Z

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Observation f7ab2310-f350-4ee5-99b0-083c060da13b · outbound

This paper cites Struc- ture and content-guided video synthesis with diffusion models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Struc- ture and content-guided video synthesis with diffusion models

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.247587Z

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Observation d48f7c0b-a2dc-41a0-89fa-a0f8a2b476a3 · outbound

This paper cites Magicanimate: Temporally consis- tent human image animation using diffusion model.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Magicanimate: Temporally consis- tent human image animation using diffusion model

Reference 5

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Observation 782ddfce-9094-4dd9-bf90-4bfec8279a41 · outbound

This paper cites ModelScope Text-to-Video Technical Report.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models ModelScope Text-to-Video Technical Report

Reference 6

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Source-reported events for the cited work

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Observation 85d137dd-d716-47fa-b804-8c46df0308b2 · outbound

This paper cites Onlyflow: Opti- cal flow based motion conditioning for video diffusion models, 2024.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Onlyflow: Opti- cal flow based motion conditioning for video diffusion models, 2024

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.217546Z

Source-reported events for the cited work

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Observation f9d16964-4ac8-4b50-aba5-5e3dd1684d14 · outbound

This paper cites Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Motion-i2v: Consistent and controllable image-to-video generation with explicit motion modeling

Reference 8

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Source-reported events for the cited work

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Observation 5b7d1489-27b7-4f54-aad1-768539c81a17 · outbound

This paper cites Optical-flow guided prompt optimization for co- herent video generation, 2025.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Optical-flow guided prompt optimization for co- herent video generation, 2025

Reference 9

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Source-reported events for the cited work

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

Observation 5daadc27-28ef-48f2-9a93-8051961969ca · outbound

This paper cites FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.549717Z

Source-reported events for the cited work

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Observation 6f4d88be-31fb-4466-816d-e728698673d7 · outbound

This paper cites Flowvid: Taming imperfect optical flows for consistent video-to-video synthesis, 2023.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Flowvid: Taming imperfect optical flows for consistent video-to-video synthesis, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.178858Z

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 b7b45ff5-e337-47bf-aa6e-471d1b9891f1 · outbound

This paper cites Conditional image-to-video generation with latent flow diffusion models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Conditional image-to-video generation with latent flow diffusion models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.164486Z

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 7bf2e7bb-11b0-4c38-81ac-8e4383bd8fb6 · outbound

This paper cites Optical-Flow Guided Prompt Optimization for Coherent Video Generation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Optical-Flow Guided Prompt Optimization for Coherent Video Generation

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:49:08.858967Z

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 eb404f66-dd79-46b4-83d6-8c9aac2ecfd4 · outbound

This paper cites Learning video stabilization using optical flow.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Learning video stabilization using optical flow

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.151154Z

Source-reported events for the cited work

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Observation 5166cad8-03ac-4d96-8c3e-37c0e4a78a74 · outbound

This paper cites Adding conditional control to text-to-image diffusion models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Adding conditional control to text-to-image diffusion models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.137704Z

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 1fb0e08d-a99d-4d73-a412-3fded2cda480 · outbound

This paper cites Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control

Reference 16

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Source-reported events for the cited work

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Observation 56c7bdc7-29a5-4377-b11d-66a570a02cac · outbound

This paper cites Draganything: Motion control for anything using entity representation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Draganything: Motion control for anything using entity representation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.124590Z

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 3ff3f426-e853-4a29-a049-c908907e1e45 · outbound

This paper cites Image conductor: Precision control for interactive video synthesis.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Image conductor: Precision control for interactive video synthesis

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.109554Z

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 15d331e9-2cac-4c36-9bf8-1b1c27da5c4e · outbound

This paper cites Motion Prompting: Controlling Video Generation with Motion Trajectories.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Motion Prompting: Controlling Video Generation with Motion Trajectories

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.588781Z

Source-reported events for the cited work

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Observation b1e90a1e-e17f-40a6-b977-d720c5bd9eb0 · outbound

This paper cites Motionctrl: A unified and flexible motion con- troller for video generation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Motionctrl: A unified and flexible motion con- troller for video generation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.093181Z

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 b9db159b-ceaa-4cdf-8cf7-9d91c6f90420 · outbound

This paper cites Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.597297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b883de0-f65c-4e7d-8ab8-712250b69dbc · outbound

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

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Sparsectrl: Adding sparse controls to text-to-video diffusion models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.078274Z

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 86b2ded8-4b73-4ca4-be49-b9ee6debf133 · outbound

This paper cites Learning to Act from Actionless Videos through Dense Correspondences.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Learning to Act from Actionless Videos through Dense Correspondences

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.605524Z

Source-reported events for the cited work

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Observation 24600266-4422-4441-bc11-111d4b261db0 · outbound

This paper cites This&That: Language-Gesture Controlled Video Generation for Robot Planning.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models This&That: Language-Gesture Controlled Video Generation for Robot Planning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.609961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af71635e-d804-48e4-9186-77757806461b · outbound

This paper cites Learning universal policies via text-guided video generation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Learning universal policies via text-guided video generation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.062712Z

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 881c7825-bc12-4680-a7ea-8dd7c527b47c · outbound

This paper cites Unisim: A neural closed-loop sensor sim- ulator.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Unisim: A neural closed-loop sensor sim- ulator

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.047804Z

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 6be90b29-d3d8-4e7b-9f61-ba1ff139b3b3 · outbound

This paper cites VideoAgent: Self-Improving Video Generation.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models VideoAgent: Self-Improving Video Generation

Reference 27

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Source-reported events for the cited work

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Observation afff4eac-caed-452b-8041-76fbfcf32cde · outbound

This paper cites FlowNet: Learning Optical Flow with Convolutional Networks.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models FlowNet: Learning Optical Flow with Convolutional Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.628406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83b095d8-1bf3-410d-b752-a891cb7af63b · outbound

This paper cites Efficient sparse- to-dense optical flow estimation using a learned basis and layers.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Efficient sparse- to-dense optical flow estimation using a learned basis and layers

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.032547Z

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 8bd5cf96-7da1-4653-8ce6-aeb9ccb36221 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Elucidating the design space of diffusion-based generative models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.638338Z

Source-reported events for the cited work

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Observation 56796982-f0f3-4eef-a1ff-aaffde9d3802 · outbound

This paper cites Dense optical tracking: Connecting the dots.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Dense optical tracking: Connecting the dots

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:09.006946Z

Source-reported events for the cited work

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Observation d6a6c667-ffa3-4e36-a1f9-7e50bb54f8fb · outbound

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

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.647325Z

Source-reported events for the cited work

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Observation 58f04020-3bd9-4cb8-898b-7d87c87b7d24 · outbound

This paper cites Bridgedata v2: A dataset for robot learning at scale.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Bridgedata v2: A dataset for robot learning at scale

Reference 33

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Source-reported events for the cited work

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Observation 69bb75bd-81bd-4a9d-aaf4-2321ecaed664 · outbound

This paper cites Film: Visual reason- ing with a general conditioning layer.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Film: Visual reason- ing with a general conditioning layer

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:08.979886Z

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 1e6d4f00-93d6-48a1-8288-a15b6e668c0d · outbound

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

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:08.965017Z

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

Observation 965a5b2b-96a0-4a04-ad74-dea1071309c1 · outbound

This paper cites Bovik, H.R.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Bovik, H.R

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:08.949881Z

Source-reported events for the cited work

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Observation c3c661d7-57bb-4493-b50c-670ce34d6d69 · outbound

This paper cites Image quality metrics: Psnr vs.

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Image quality metrics: Psnr vs

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:08.935241Z

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 131fe764-c74b-444b-be34-0d959ee61567 · outbound

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

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models The unreasonable effectiveness of deep features as a perceptual metric

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:49:08.920835Z

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 43cbee19-e1e2-4b92-a8b7-6b0131d29c70 · outbound

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

FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models Towards Accurate Generative Models of Video: A New Metric & Challenges

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T11:49:08.680321Z

Source-reported events for the cited work

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Pith citing papers

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