MotiF: Making Text Count in Image Animation with Motion Focal Loss | Paper Citation Record

Observation 5f8feaa9-cf3b-43e9-a30c-c2facc3da2bd · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation

Reference 1

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Observation da23cae8-7d2e-4b07-afec-6a12ae46f14f · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Frozen in time: A joint video and image encoder for end-to-end retrieval

Reference 2

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Observation 74fbbf72-d4d4-4804-86e7-bebc7489e8c6 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

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Observation b9e03d42-cbe0-4baf-a636-f1f8e651acf7 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Align your latents: High-resolution video synthesis with la- tent diffusion models

Reference 4

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Observation 4bf75c4d-7f4b-4156-a00c-cea98946bcc6 · outbound

This paper cites Video generation models as world simulators.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Video generation models as world simulators

Reference 5

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Observation bf199bd8-4545-4d3c-8709-440476e7bc59 · outbound

This paper cites Animat- ing general image with large visual motion model.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Animat- ing general image with large visual motion model

Reference 6

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Observation 32733eb9-4450-4ecb-b3f0-6df34be1be7f · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss VideoCrafter1: Open Diffusion Models for High-Quality Video Generation

Reference 7

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Observation d54ab77c-b092-421e-a38a-7ca45abfbd3e · outbound

This paper cites Videocrafter2: Overcoming data limitations for high-quality video diffusion models, 2024.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Videocrafter2: Overcoming data limitations for high-quality video diffusion models, 2024

Reference 8

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Observation 7bce2c6f-c0a6-4870-b07e-767fb72f33a2 · outbound

This paper cites Seine: Short-to-long video diffu- sion model for generative transition and prediction.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Seine: Short-to-long video diffu- sion model for generative transition and prediction

Reference 9

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Observation 17e117f3-301d-4a44-baff-cb78e7f84eef · outbound

This paper cites Livephoto: Real image animation with text-guided motion control.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Livephoto: Real image animation with text-guided motion control

Reference 10

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Observation f4ed73d5-72b2-471f-8811-223aa048cb00 · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 11

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Observation 49397f0e-404f-4899-81a9-5da62a0eb0a4 · outbound

This paper cites Animateanything: Fine- grained open domain image animation with motion guid- ance.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Animateanything: Fine- grained open domain image animation with motion guid- ance

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.455046Z

Source-reported events for the cited work

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Observation 0627cba8-ed4e-43c1-bd08-dc47d10d7ccb · outbound

This paper cites AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI.

MotiF: Making Text Count in Image Animation with Motion Focal Loss AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T10:49:06.518012Z

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Observation 88a32a85-c904-41fc-94e1-e40979e04783 · outbound

This paper cites Preserve your own correlation: A noise prior for video diffusion models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Preserve your own correlation: A noise prior for video diffusion models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T10:49:06.523213Z

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Observation 586eaac0-db1c-4445-b16a-69e6df17719f · outbound

This paper cites Emu video: Factoriz- ing text-to-video generation by explicit image conditioning.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Emu video: Factoriz- ing text-to-video generation by explicit image conditioning

Reference 15

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Observation 7955499d-8107-4545-9870-6a393211c43b · outbound

This paper cites I2v-adapter: A general image-to-video adapter for diffusion models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss I2v-adapter: A general image-to-video adapter for diffusion models

Reference 16

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Source-reported events for the cited work

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Observation aa24b8f4-5bac-4bf0-8c80-dbabc9fbd52c · outbound

This paper cites Denoising dif- fusion probabilistic models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Denoising dif- fusion probabilistic models

Reference 17

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Observation 560fb056-dcc2-4556-998d-8a1405e52b7e · outbound

This paper cites Video dif- fusion models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Video dif- fusion models

Reference 18

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Observation d04e818a-69d6-49fa-9824-370b937f0d58 · outbound

This paper cites Make it move: Controllable image-to-video generation with text descrip- tions.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Make it move: Controllable image-to-video generation with text descrip- tions

Reference 19

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Observation 12675c13-be6a-4cb3-bc47-a716c29613bf · outbound

This paper cites VBench: Com- prehensive benchmark suite for video generative models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss VBench: Com- prehensive benchmark suite for video generative models

Reference 20

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Observation 198c63aa-93f9-469f-a78b-ad352445e59b · outbound

This paper cites Vbench: Comprehensive bench- mark suite for video generative models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Vbench: Comprehensive bench- mark suite for video generative models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.341698Z

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Observation c1e73ecd-7829-48da-bb5b-4573dde9b8d0 · outbound

This paper cites VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation.

MotiF: Making Text Count in Image Animation with Motion Focal Loss VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generation

Reference 22

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Observation 343e7d14-1382-4eb7-a5b8-5cf077a2a8b9 · outbound

This paper cites Physgen: Rigid-body physics-grounded image- to-video generation.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Physgen: Rigid-body physics-grounded image- to-video generation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.324414Z

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Observation ffc46977-a43e-49ae-a806-a95416d3611a · outbound

This paper cites Evalcrafter: Benchmarking and eval- uating large video generation models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Evalcrafter: Benchmarking and eval- uating large video generation models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T10:49:06.567962Z

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Observation a7fca664-894b-4a25-8566-d6cff4a41ead · outbound

This paper cites Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Cinemo: Consistent and Controllable Image Animation with Motion Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T10:49:06.573171Z

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Observation 75213b1f-7a3a-41b8-b899-4ea7df769d5f · outbound

This paper cites Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Follow-Your-Click: Open-domain Regional Image Animation via Short Prompts

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T10:49:06.578398Z

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Observation 5c4a0bb4-37e9-4cd9-b919-a87fdd65bcee · outbound

This paper cites Sync-draw: Automatic video generation using deep recurrent attentive architectures.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Sync-draw: Automatic video generation using deep recurrent attentive architectures

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.295710Z

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Observation ce76bf80-9736-453e-bc6a-cf06adfcfbb1 · outbound

This paper cites Ti2v-zero: Zero-shot image condition- ing for text-to-video diffusion models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Ti2v-zero: Zero-shot image condition- ing for text-to-video diffusion models

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.277813Z

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Observation 1ee9b71f-6541-4f7d-b6e0-ea914530cfaf · outbound

This paper cites Scalable diffusion models with transformers.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Scalable diffusion models with transformers

Reference 29

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Observation 3b34f342-e379-4337-8539-e45aa07638f1 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Movie Gen: A Cast of Media Foundation Models

Reference 30

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Observation 0c3b48a3-4cec-4060-935e-db410c87f3c0 · outbound

This paper cites Hier- archical spatio-temporal decoupling for text-to-video gener- ation.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Hier- archical spatio-temporal decoupling for text-to-video gener- ation

Reference 31

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Observation 456fba28-d5e7-40d7-a7c2-35b61bdecc12 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

MotiF: Making Text Count in Image Animation with Motion Focal Loss SAM 2: Segment Anything in Images and Videos

Reference 32

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Observation 1851ffcd-1ad3-4806-8e0e-b75bdf7dc961 · outbound

This paper cites ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation.

MotiF: Making Text Count in Image Animation with Motion Focal Loss ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation

Reference 33

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Observation c855f6b7-85a3-4d4b-944e-4428cd9f4637 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss High-resolution image synthesis with latent diffusion models

Reference 34

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Observation 95ab1552-a16b-4c80-b701-d3549cbb9a92 · outbound

This paper cites Focal loss for dense ob- ject detection.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Focal loss for dense ob- ject detection

Reference 35

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Observation fb6607c7-825f-4d07-9c73-fb26afa6bdc2 · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Progressive Distillation for Fast Sampling of Diffusion Models

Reference 36

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Observation d27b60d5-83a2-431d-9a44-6b2ee793c7d2 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Make-A-Video: Text-to-Video Generation without Text-Video Data

Reference 37

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Observation da056e74-be47-4220-b25d-961c3f577954 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Deep unsupervised learning using nonequilibrium thermodynamics

Reference 38

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Observation c621ec9d-17c4-485e-9852-666146dbef3d · outbound

This paper cites Denois- ing diffusion implicit models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Denois- ing diffusion implicit models

Reference 39

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Observation 5466c700-6529-41f7-b510-725dfd94d289 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Score-Based Generative Modeling through Stochastic Differential Equations

Reference 40

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Observation 4618c896-8899-46b6-a223-673af3e2432e · outbound

This paper cites UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.

MotiF: Making Text Count in Image Animation with Motion Focal Loss UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild

Reference 41

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Observation 9b8b7f74-b3ab-44d4-bd90-2016e500b1f2 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Raft: Recurrent all-pairs field transforms for optical flow

Reference 42

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Observation 78a763d8-6539-4f4b-a808-3efc8e5682ad · outbound

This paper cites Microcinema: A divide-and- conquer approach for text-to-video generation.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Microcinema: A divide-and- conquer approach for text-to-video generation

Reference 43

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Observation 44c9d339-6e15-4eaf-b087-3df805681924 · outbound

This paper cites Dreamvideo: Composing your dream videos with customized subject and motion.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Dreamvideo: Composing your dream videos with customized subject and motion

Reference 44

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Observation 3f8302a7-dfa1-4c62-8f72-61e94fd9df06 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation

Reference 45

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Observation 0ceb8851-790f-4283-8b4a-c5c16195bb4b · outbound

This paper cites Dynamicrafter: Animating open-domain images with video diffusion priors.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Dynamicrafter: Animating open-domain images with video diffusion priors

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.149060Z

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Observation 07a1458d-3778-4676-8bc7-48a26c7abdd2 · outbound

This paper cites Msr-vtt: A large video description dataset for bridging video and language.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Msr-vtt: A large video description dataset for bridging video and language

Reference 47

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Observation 5cb96f43-0e99-4955-a58a-98b8842049d8 · outbound

This paper cites Motion-Conditioned Image Animation for Video Editing.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Motion-Conditioned Image Animation for Video Editing

Reference 48

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Observation f3641f0b-fa18-4d10-9283-0702a1bc8f2c · outbound

This paper cites Zero-shot controllable image-to-video animation via motion decomposition.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Zero-shot controllable image-to-video animation via motion decomposition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.113704Z

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Observation f28483e1-c41b-4d32-a134-fc6d89f71144 · outbound

This paper cites I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Reference 50

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Observation 168f8b80-745a-4b15-995b-140bdf2a3a62 · outbound

This paper cites Pia: Your personalized image animator via plug-and-play modules in text-to-image models.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Pia: Your personalized image animator via plug-and-play modules in text-to-image models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:49:07.095893Z

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Observation 0f481d93-ab49-4145-9808-ea9fe352c893 · outbound

This paper cites Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model

Reference 52

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Observation e25f6935-7bbd-4cc5-abc3-6af1b4c76252 · outbound

This paper cites Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model.

MotiF: Making Text Count in Image Animation with Motion Focal Loss Identifying and Solving Conditional Image Leakage in Image-to-Video Diffusion Model

Reference 53

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Observation 2672eec0-28a5-49b0-943f-c08c0e4192e4 · outbound

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

MotiF: Making Text Count in Image Animation with Motion Focal Loss MagicVideo: Efficient Video Generation With Latent Diffusion Models

Reference 54

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