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Paper Citation Record · LEDGER

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators

As of 7 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 0 inbound Pith citation observations for arXiv:2506.19348.

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

pith.paper-citation-record.v1
2506.19348 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:12:56.741852Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation c308e995-3574-4fb0-8fd3-258ef5820807 · outbound

This paper cites T2v-turbo-v2: Enhancing video generation model post-training through data, reward, and conditional guidance design.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators T2v-turbo-v2: Enhancing video generation model post-training through data, reward, and conditional guidance design

Reference 1

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Observation af7d22ae-6d70-4cd2-b6c9-30f6d77976c1 · outbound

This paper cites AnimateDiff-Lightning: Cross-Model Diffusion Distillation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators AnimateDiff-Lightning: Cross-Model Diffusion Distillation

Reference 2

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Observation 4f28baa2-ac71-4d4a-9152-fdc37dbfe28a · outbound

This paper cites Video diffusion models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Video diffusion models

Reference 3

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Observation 9dc43bd4-44ac-4368-8603-eb8b100ad278 · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 4

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Observation 8254067d-566c-4acb-bb72-5ebb38885af2 · outbound

This paper cites Veo.Placeholder Journal, 2024.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Veo.Placeholder Journal, 2024

Reference 5

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1f252ec3-0fb4-480d-9ded-9e901dfe79c2 · outbound

This paper cites Video generation models as world simulators, 2024.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Video generation models as world simulators, 2024

Reference 6

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Observation 789be283-39b0-44f1-8fed-055e3d76aa16 · outbound

This paper cites T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback

Reference 7

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Observation bc978bc8-2445-4511-bdd5-56f660f9dacd · outbound

This paper cites Vmc: Video motion customization using temporal attention adaption for text-to-video diffusion models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Vmc: Video motion customization using temporal attention adaption for text-to-video diffusion models

Reference 8

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Observation 76ca366f-ba44-4cf5-a47b-626e4207022c · outbound

This paper cites Motiondirector: Motion customization of text-to-video diffusion models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Motiondirector: Motion customization of text-to-video diffusion models

Reference 9

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Observation 0a88eba5-68b2-4bfd-96ed-8daf1d44bd4f · outbound

This paper cites Motion Inversion for Video Customization.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Motion Inversion for Video Customization

Reference 10

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Observation 9a89f376-e36c-4602-be0a-3a09ca30806b · outbound

This paper cites ControlVideo: Training-free Controllable Text-to-Video Generation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators ControlVideo: Training-free Controllable Text-to-Video Generation

Reference 11

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Observation 79d71bbf-8f52-47b5-9556-064f0324bc65 · outbound

This paper cites Controlvideo: conditional control for one-shot text-driven video editing and beyond.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Controlvideo: conditional control for one-shot text-driven video editing and beyond

Reference 12

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 10f03514-d42c-4e74-83a9-e630447132f2 · outbound

This paper cites Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning

Reference 13

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Observation f0b4d9c8-0e22-4a6f-8e2d-683e68bf686c · outbound

This paper cites MotionClone: Training-Free Motion Cloning for Controllable Video Generation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators MotionClone: Training-Free Motion Cloning for Controllable Video Generation

Reference 14

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source=pdf_text observed=2026-08-06T23:12:53.367505Z digest=sha256:149122ce5685548d241ab1adcd59178aa2c7c909aa19f8c15041d1464eeeeab1

Observation 732724c7-f16f-4eaa-9ffd-ff322b7706ca · outbound

This paper cites Space-time diffusion features for zero-shot text-driven motion transfer.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Space-time diffusion features for zero-shot text-driven motion transfer

Reference 15

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Observation d623f30d-270c-4fc5-8104-908747d277c1 · outbound

This paper cites Text2video-zero: Text-to-image diffusion models are zero-shot video generators.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Text2video-zero: Text-to-image diffusion models are zero-shot video generators

Reference 16

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Observation dd8edbfb-15db-4021-a9c4-8afd956c344a · outbound

This paper cites Lora: Low-rank adaptation of large language models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Lora: Low-rank adaptation of large language models

Reference 17

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Observation c27cab79-221e-40ca-a055-a3ac106e0e40 · outbound

This paper cites VideoLCM: Video Latent Consistency Model.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators VideoLCM: Video Latent Consistency Model

Reference 18

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Observation 228ae53e-49b4-4300-9084-b61c149ee2f7 · outbound

This paper cites Consistency models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Consistency models

Reference 19

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Observation dd40d515-76d9-40b9-9645-ded81ce0fecd · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 20

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Observation c9d618f4-80d6-4c02-b396-41becfb5013d · outbound

This paper cites Denoising Diffusion Implicit Models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Denoising Diffusion Implicit Models

Reference 21

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Observation 9f6434ea-69c4-4e5b-9bfb-938297cc6ac7 · outbound

This paper cites Denoising diffusion probabilistic models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Denoising diffusion probabilistic models

Reference 22

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Observation f632f14a-5dc7-4aeb-85b2-1d211977c8f4 · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Score-Based Generative Modeling through Stochastic Differential Equations

Reference 23

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Observation f7907c09-61f0-47c0-a702-546ae30ddf79 · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 24

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Observation 1e42d335-9dee-4def-9a77-aaa1eb89416f · outbound

This paper cites Flux.1-schnell, 2024.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Flux.1-schnell, 2024

Reference 25

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Observation 34e414e5-1b4d-4a34-ba8a-0002f19d2c53 · outbound

This paper cites Scaling rectified flow transform- ers for high-resolution image synthesis.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Scaling rectified flow transform- ers for high-resolution image synthesis

Reference 26

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Observation 175ffe19-c47c-465b-b753-071c73749755 · outbound

This paper cites IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation

Reference 27

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Observation 1e2cddf8-c89b-4752-b124-f47521663ec6 · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators High- resolution image synthesis with latent diffusion models

Reference 28

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Observation 7f059beb-d636-4fb9-9f03-85d28e682e8d · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Photorealistic text-to-image diffusion models with deep language understanding

Reference 29

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Observation 9e24ebef-8d5f-4b41-8b41-e3175654d51c · outbound

This paper cites Improving image generation with better captions.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Improving image generation with better captions

Reference 30

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Observation c9a182ec-fa93-4192-8679-4d44a226aaa8 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 31

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Observation 8be31ac5-c80c-469f-ac31-5f5fad138dc2 · outbound

This paper cites Introducing gen-3 alpha: A new frontier for video generation, 2024.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Introducing gen-3 alpha: A new frontier for video generation, 2024

Reference 32

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Observation 80257bbc-302b-4a80-9a68-458a367369f5 · outbound

This paper cites 3d u-net: learning dense volumetric segmentation from sparse annotation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators 3d u-net: learning dense volumetric segmentation from sparse annotation

Reference 33

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 24c50864-053f-417d-9a56-fe648de29c7d · outbound

This paper cites Kingma, Ben Poole, Mohammad Norouzi, David J.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Kingma, Ben Poole, Mohammad Norouzi, David J

Reference 34

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Observation 1fefe9de-7d8d-4f32-9b23-35b5894fba42 · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 35

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Observation 371a2360-7af1-46ad-9c52-5a9060a0fbe8 · outbound

This paper cites Latent Video Diffusion Models for High-Fidelity Long Video Generation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Latent Video Diffusion Models for High-Fidelity Long Video Generation

Reference 36

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Observation 179da4a4-8067-40dc-a7be-b6341f41d63b · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Align your latents: High-resolution video synthesis with latent diffusion models

Reference 37

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Observation cd5fc8ea-1a57-49a3-9e22-0854dbbb30a6 · outbound

This paper cites MagicVideo: Efficient Video Generation With Latent Diffusion Models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators MagicVideo: Efficient Video Generation With Latent Diffusion Models

Reference 38

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Observation 710c6a17-f5c7-4ece-8884-d69625c1c214 · outbound

This paper cites Advancing high-resolution video-language representation with large-scale video transcriptions.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Advancing high-resolution video-language representation with large-scale video transcriptions

Reference 40

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Source-reported events for the cited work

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Observation 2baba955-4c7d-4847-a0fe-127a20006263 · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 41

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Observation f68d58bf-ba3c-4ca0-ae6e-4327caf53e4f · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Videocrafter2: Overcoming data limitations for high-quality video diffusion models

Reference 42

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Observation ab566081-cc25-4445-8d2d-c88e8db94a3c · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 43

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Source-reported events for the cited work

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Observation 3dd47854-2249-47a9-8bd2-47054ac6ae6d · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Adding conditional control to text-to-image diffusion models

Reference 44

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Source-reported events for the cited work

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Observation 44af82cb-9e22-420f-b1a2-63117a1473b9 · outbound

This paper cites Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss

Reference 45

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Observation 44602c8a-bd12-46d4-9c27-78edb392bb2f · outbound

This paper cites Classifier-Free Diffusion Guidance.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Classifier-Free Diffusion Guidance

Reference 46

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source=pdf_text observed=2026-08-06T23:12:55.871971Z digest=sha256:61d4aa58935a76a28c83b426a4e3d875034acb5ebf025f2db55e5c01902352b6

Observation 29340849-dc59-49fe-b496-844c1a841010 · outbound

This paper cites Freedom: Training- free energy-guided conditional diffusion model.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Freedom: Training- free energy-guided conditional diffusion model

Reference 47

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:55.939369Z digest=sha256:cdf05aee6156fd96a0076f315b04c31f3478c1032ac02c39e2b25a433ca23579

Observation bed397cb-b5ef-4514-b69c-fb0072083aeb · outbound

This paper cites TFG: Unified Training-Free Guidance for Diffusion Models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators TFG: Unified Training-Free Guidance for Diffusion Models

Reference 48

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Source-reported events for the cited work

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Observation 1c345922-f148-4287-98c9-565dc9467652 · outbound

This paper cites Animatelcm: Computation-efficient personalized style video gener- ation without personalized video data.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Animatelcm: Computation-efficient personalized style video gener- ation without personalized video data

Reference 49

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:56.054783Z digest=sha256:d72f52dc904dab1598301a743464f61fc6d198b1cb56d0e08b19058b5055afe3

Observation 2f3d28d6-277d-4309-bde6-3c2fed97b2ea · outbound

This paper cites Distilling the Knowledge in a Neural Network.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Distilling the Knowledge in a Neural Network

Reference 50

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Observation b781a28e-e647-42bd-ae8c-f9c95bbd8079 · outbound

This paper cites FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators FreeTraj: Tuning-Free Trajectory Control in Video Diffusion Models

Reference 51

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Observation d2c75e99-5f0b-4b3a-85c1-eb172c2c2e02 · outbound

This paper cites FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators FreeNoise: Tuning-Free Longer Video Diffusion via Noise Rescheduling

Reference 52

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Observation 49f39f3c-53a4-40ed-ab5c-9f32f5a84faf · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators The 2017 DAVIS Challenge on Video Object Segmentation

Reference 53

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T23:12:56.390119Z digest=sha256:4b111101d8b3419b1a99608c77f6015504c27a9bc6d213ee31b44b63a04959e8

Observation 2cd3c1f6-1cdf-4a52-a45b-75434d4b7513 · outbound

This paper cites Frozen in time: A joint video and image encoder for end-to-end retrieval.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 54

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T23:12:56.484453Z digest=sha256:d04a4e8c324334c4bf63edddeae8e56766c57328e1c7efa8ff462345b7784201

Observation 20ef94df-ec9f-4b3a-ba64-c627bbd88355 · outbound

This paper cites Learning transferable visual models from natural language supervision.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Learning transferable visual models from natural language supervision

Reference 55

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:56.576343Z digest=sha256:31596170b35dadc559e2a8149bf9d17bb6982f6819d857f3dfaaf5e8892461f1

Observation 6cebe02b-13fc-447f-8885-6ac74cc59824 · outbound

This paper cites Cotracker: It is better to track together.

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Cotracker: It is better to track together

Reference 56

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T23:12:56.605248Z digest=sha256:496a453fc2d8d69efa71fc7292b77a6aaabe5fac2d5f2a981e77fb6b8fa66ae0

Observation 81b9b366-d79f-4586-99df-5091f5df1b2e · outbound

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

When Distillation Breaks Motion Control: Restoring Generative Trajectories for Fast Video Generators Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 57

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raw_fallback, observed 2026-08-06T23:12:57.453133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T23:12:56.741852Z digest=sha256:6fa7ba2ec26f460094f7e936402450b1b88db969bad3ccfa3b425aa3e9c3ebea

Pith citing papers

No inbound Pith citation observations are available.