Paper Citation Record · LEDGER

Learning Flow Fields in Attention for Controllable Person Image Generation

As of 19 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 5 inbound Pith citation observations for arXiv:2412.08486.

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

pith.paper-citation-record.v1
2412.08486 v2

Coverage vector

measured 76 of 76 reference resolution

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measured 81 of 81 standing notices

One-hop event checks from named stored sources.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T07:51:29.272825Z

Reference resolution

76 of 76 outbound references displayed

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External citation measurements

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Outbound references

Observation 40a7888b-e5d6-47c3-a782-1ba869cdeb7a · outbound

This paper cites write newline.

Learning Flow Fields in Attention for Controllable Person Image Generation write newline

Reference 1

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Observation 177a68e5-6f8c-4918-9afc-ada51c544502 · outbound

This paper cites write newline.

Learning Flow Fields in Attention for Controllable Person Image Generation write newline

Reference 2

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Observation 9e7f4272-5164-466a-9bbe-4c69777f0d3c · outbound

This paper cites Pose with style: Detail-preserving pose-guided image synthesis with conditional stylegan.

Learning Flow Fields in Attention for Controllable Person Image Generation Pose with style: Detail-preserving pose-guided image synthesis with conditional stylegan

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.395291Z

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Observation a2b4ddb0-3b17-459f-95f8-f3bf84f4f99e · outbound

This paper cites Person image synthesis via denoising diffusion model.

Learning Flow Fields in Attention for Controllable Person Image Generation Person image synthesis via denoising diffusion model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.374675Z

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Observation abc13210-801e-45a2-8111-94f43df68998 · outbound

This paper cites Demystifying mmd gans.

Learning Flow Fields in Attention for Controllable Person Image Generation Demystifying mmd gans

Reference 5

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Observation de230ad7-d180-486d-bcce-2385f45af77e · outbound

This paper cites Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing.

Learning Flow Fields in Attention for Controllable Person Image Generation Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:45.732117Z

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Observation 7507c3f1-b6cd-4160-88a4-1a5036644ac2 · outbound

This paper cites Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.

Learning Flow Fields in Attention for Controllable Person Image Generation Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.332069Z

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Observation ebf96bc8-6b29-4b4e-9b2a-c3583f1dacc7 · outbound

This paper cites Viton-hd: High-resolution virtual try-on via misalignment-aware normalization.

Learning Flow Fields in Attention for Controllable Person Image Generation Viton-hd: High-resolution virtual try-on via misalignment-aware normalization

Reference 8

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Observation 6d17d570-f932-457d-8e47-50c93e526e7c · outbound

This paper cites Improving diffusion models for authentic virtual try-on in the wild.

Learning Flow Fields in Attention for Controllable Person Image Generation Improving diffusion models for authentic virtual try-on in the wild

Reference 9

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Observation 5252d2e1-b55c-4fe1-be58-561fbd8993be · outbound

This paper cites CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models.

Learning Flow Fields in Attention for Controllable Person Image Generation CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models

Reference 10

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Observation 14a3a875-5ba1-44e8-92ba-80ac4ae55d95 · outbound

This paper cites Learning Garment DensePose for Robust Warping in Virtual Try-On.

Learning Flow Fields in Attention for Controllable Person Image Generation Learning Garment DensePose for Robust Warping in Virtual Try-On

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:45.760736Z

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Observation 430c14fc-5379-49a9-87d6-8f7d5d7629ad · outbound

This paper cites Street tryon: Learning in-the-wild virtual try-on from unpaired person images.

Learning Flow Fields in Attention for Controllable Person Image Generation Street tryon: Learning in-the-wild virtual try-on from unpaired person images

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.285555Z

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Observation 1b31bc5f-c917-4a8f-9852-1a543f3bd5aa · outbound

This paper cites Vision transformers need registers.

Learning Flow Fields in Attention for Controllable Person Image Generation Vision transformers need registers

Reference 13

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Observation 4839b6c7-2a12-48ca-8d44-aa3e513a3b81 · outbound

This paper cites Masked diffusion transformer is a strong image synthesizer.

Learning Flow Fields in Attention for Controllable Person Image Generation Masked diffusion transformer is a strong image synthesizer

Reference 14

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Observation 3999639e-a9a5-4bc4-b7c5-51aeb956290e · outbound

This paper cites Disentangled cycle consistency for highly-realistic virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Disentangled cycle consistency for highly-realistic virtual try-on

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.236574Z

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Observation 3cdaaf3c-8307-4fb3-a14e-632d6901b4d8 · outbound

This paper cites Parser-free virtual try-on via distilling appearance flows.

Learning Flow Fields in Attention for Controllable Person Image Generation Parser-free virtual try-on via distilling appearance flows

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.220323Z

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Observation 9c74b3de-c65d-467c-a9c8-501497b83aa4 · outbound

This paper cites Taming the power of diffusion models for high-quality virtual try-on with appearance flow.

Learning Flow Fields in Attention for Controllable Person Image Generation Taming the power of diffusion models for high-quality virtual try-on with appearance flow

Reference 17

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Observation 897ed3c1-0d98-442f-a0ae-9f5ebadad665 · outbound

This paper cites Densepose: Dense human pose estimation in the wild.

Learning Flow Fields in Attention for Controllable Person Image Generation Densepose: Dense human pose estimation in the wild

Reference 18

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Observation 126674bd-cf8c-4c90-9eb5-4abfd00a9201 · outbound

This paper cites Focus on your instruction: Fine-grained and multi-instruction image editing by attention modulation.

Learning Flow Fields in Attention for Controllable Person Image Generation Focus on your instruction: Fine-grained and multi-instruction image editing by attention modulation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.163415Z

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Observation 77cf9193-4c31-46f9-8002-a228edd5a938 · outbound

This paper cites Controllable person image synthesis with pose-constrained latent diffusion.

Learning Flow Fields in Attention for Controllable Person Image Generation Controllable person image synthesis with pose-constrained latent diffusion

Reference 20

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Observation 610c7e2b-4c95-4bc6-9404-ee0070ffeb65 · outbound

This paper cites Style-based global appearance flow for virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Style-based global appearance flow for virtual try-on

Reference 21

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Observation 647f6e61-7fbd-48a7-b302-26478b4d162e · outbound

This paper cites Prompt-to-prompt image editing with cross-attention control.

Learning Flow Fields in Attention for Controllable Person Image Generation Prompt-to-prompt image editing with cross-attention control

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.112064Z

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Observation 620aa845-af47-4719-8de8-3596a16bd92b · outbound

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

Learning Flow Fields in Attention for Controllable Person Image Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 23

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Observation 4ae16f0e-88d9-43bc-b04f-bca5acf28919 · outbound

This paper cites Denoising diffusion probabilistic models.

Learning Flow Fields in Attention for Controllable Person Image Generation Denoising diffusion probabilistic models

Reference 24

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Observation 5228dbed-0735-441b-a07a-1bf8fbb98f38 · outbound

This paper cites Stableviton: Learning semantic correspondence with latent diffusion model for virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Stableviton: Learning semantic correspondence with latent diffusion model for virtual try-on

Reference 25

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Observation 19e853dd-3b5c-464e-804f-88b8db636697 · outbound

This paper cites Auto-Encoding Variational Bayes.

Learning Flow Fields in Attention for Controllable Person Image Generation Auto-Encoding Variational Bayes

Reference 26

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Observation 866832fb-835b-4899-9312-e0fd292b4672 · outbound

This paper cites The Role of ImageNet Classes in Fr\'echet Inception Distance.

Learning Flow Fields in Attention for Controllable Person Image Generation The Role of ImageNet Classes in Fr\'echet Inception Distance

Reference 27

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Observation e2f468e8-f46d-4239-a000-a9cd708cd9f5 · outbound

This paper cites High-resolution virtual try-on with misalignment and occlusion-handled conditions.

Learning Flow Fields in Attention for Controllable Person Image Generation High-resolution virtual try-on with misalignment and occlusion-handled conditions

Reference 28

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This paper cites Dense intrinsic appearance flow for human pose transfer.

Learning Flow Fields in Attention for Controllable Person Image Generation Dense intrinsic appearance flow for human pose transfer

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:47.045281Z

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Observation 0538c251-10b0-414d-b533-74f53a9dce2f · outbound

This paper cites Faster diffusion via temporal attention decomposition.

Learning Flow Fields in Attention for Controllable Person Image Generation Faster diffusion via temporal attention decomposition

Reference 30

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Observation e7443e56-e4e6-4fef-ad1c-c5f77e22bfa3 · outbound

This paper cites Deepfashion: Powering robust clothes recognition and retrieval with rich annotations.

Learning Flow Fields in Attention for Controllable Person Image Generation Deepfashion: Powering robust clothes recognition and retrieval with rich annotations

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.999399Z

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Observation 239acd58-8249-4d2a-8160-a02758f869ad · outbound

This paper cites Decoupled Weight Decay Regularization.

Learning Flow Fields in Attention for Controllable Person Image Generation Decoupled Weight Decay Regularization

Reference 32

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Observation 1b012506-21c4-4017-86f1-f8d784edb3eb · outbound

This paper cites Coarse-to-fine latent diffusion for pose-guided person image synthesis.

Learning Flow Fields in Attention for Controllable Person Image Generation Coarse-to-fine latent diffusion for pose-guided person image synthesis

Reference 33

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Observation 4998e4c7-b6bc-4ba1-9bd5-66dba861eae5 · outbound

This paper cites Pose guided person image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Pose guided person image generation

Reference 34

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Observation aeefb403-fdeb-4b27-a10e-66260c76adf9 · outbound

This paper cites Controllable person image synthesis with attribute-decomposed gan.

Learning Flow Fields in Attention for Controllable Person Image Generation Controllable person image synthesis with attribute-decomposed gan

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.926659Z

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Observation d70917ba-8eb6-4335-9c96-42d2766e87ab · outbound

This paper cites Dress code: High-resolution multi-category virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Dress code: High-resolution multi-category virtual try-on

Reference 36

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This paper cites Ladi-vton: Latent diffusion textual-inversion enhanced virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Ladi-vton: Latent diffusion textual-inversion enhanced virtual try-on

Reference 37

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This paper cites Picture: Photorealistic virtual try-on from unconstrained designs.

Learning Flow Fields in Attention for Controllable Person Image Generation Picture: Photorealistic virtual try-on from unconstrained designs

Reference 38

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This paper cites Dinov2: Learning robust visual features without supervision.

Learning Flow Fields in Attention for Controllable Person Image Generation Dinov2: Learning robust visual features without supervision

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.873338Z

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Observation 963de2b3-e501-4e1c-bb03-a891f7954f36 · outbound

This paper cites Scalable diffusion models with transformers.

Learning Flow Fields in Attention for Controllable Person Image Generation Scalable diffusion models with transformers

Reference 40

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This paper cites Cross-view masked diffusion transformers for person image synthesis.

Learning Flow Fields in Attention for Controllable Person Image Generation Cross-view masked diffusion transformers for person image synthesis

Reference 41

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This paper cites Grounded text-to-image synthesis with attention refocusing.

Learning Flow Fields in Attention for Controllable Person Image Generation Grounded text-to-image synthesis with attention refocusing

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.828704Z

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Observation 5950ee4c-3a86-4702-a3fa-7215106aab8a · outbound

This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

Learning Flow Fields in Attention for Controllable Person Image Generation Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.805524Z

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

Learning Flow Fields in Attention for Controllable Person Image Generation Learning transferable visual models from natural language supervision

Reference 44

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Learning Flow Fields in Attention for Controllable Person Image Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 45

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This paper cites Deep image spatial transformation for person image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Deep image spatial transformation for person image generation

Reference 46

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This paper cites Neural texture extraction and distribution for controllable person image synthesis.

Learning Flow Fields in Attention for Controllable Person Image Generation Neural texture extraction and distribution for controllable person image synthesis

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.741391Z

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

Learning Flow Fields in Attention for Controllable Person Image Generation High-resolution image synthesis with latent diffusion models

Reference 48

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This paper cites U-net: Convolutional networks for biomedical image segmentation.

Learning Flow Fields in Attention for Controllable Person Image Generation U-net: Convolutional networks for biomedical image segmentation

Reference 49

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This paper cites Learning realistic human reposing using cyclic self-supervision with 3d shape, pose, and appearance consistency.

Learning Flow Fields in Attention for Controllable Person Image Generation Learning realistic human reposing using cyclic self-supervision with 3d shape, pose, and appearance consistency

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.702438Z

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Observation 8d362974-30af-4146-935f-c3a214ba6b32 · outbound

This paper cites Towards squeezing-averse virtual try-on via sequential deformation.

Learning Flow Fields in Attention for Controllable Person Image Generation Towards squeezing-averse virtual try-on via sequential deformation

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.683479Z

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Observation c62091ca-c080-4fa3-ad8c-c8eddd40c3e4 · outbound

This paper cites Deformable gans for pose-based human image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Deformable gans for pose-based human image generation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.659873Z

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This paper cites Conditional Frechet Inception Distance.

Learning Flow Fields in Attention for Controllable Person Image Generation Conditional Frechet Inception Distance

Reference 53

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This paper cites OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person.

Learning Flow Fields in Attention for Controllable Person Image Generation OutfitAnyone: Ultra-high Quality Virtual Try-On for Any Clothing and Any Person

Reference 54

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Observation bdd22b20-1c7a-400c-ad96-31cde8198776 · outbound

This paper cites Xinggan for person image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Xinggan for person image generation

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.642906Z

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Observation 9c5cada7-c5c3-4b58-9f4a-9f539e967d0f · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Learning Flow Fields in Attention for Controllable Person Image Generation Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:45.997884Z

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Observation ef3f956b-3e4a-47a6-8aec-f0e9d21e61f2 · outbound

This paper cites Improving virtual try-on with garment-focused diffusion models.

Learning Flow Fields in Attention for Controllable Person Image Generation Improving virtual try-on with garment-focused diffusion models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.003432Z

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Observation cc64e7e3-769d-498f-825b-4ce6928f9f49 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Learning Flow Fields in Attention for Controllable Person Image Generation Image quality assessment: from error visibility to structural similarity

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.008798Z

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Observation 3e829d52-b3c6-4a35-8d4d-0ba571d646cb · outbound

This paper cites Fastcomposer: Tuning-free multi-subject image generation with localized attention.

Learning Flow Fields in Attention for Controllable Person Image Generation Fastcomposer: Tuning-free multi-subject image generation with localized attention

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.592259Z

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Observation a0dc2eeb-6519-4219-a667-3bc2f3d62e11 · outbound

This paper cites Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion.

Learning Flow Fields in Attention for Controllable Person Image Generation Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.572242Z

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Observation ca8c1d17-19e5-4c0c-bb8c-6daf66473f1b · outbound

This paper cites Gp-vton: Towards general purpose virtual try-on via collaborative local-flow global-parsing learning.

Learning Flow Fields in Attention for Controllable Person Image Generation Gp-vton: Towards general purpose virtual try-on via collaborative local-flow global-parsing learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.554444Z

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Observation 52ede68e-4fe4-444d-8876-d2a08c16d25a · outbound

This paper cites OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.030803Z

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Observation 1f3ca33e-fc69-453e-b029-1b8081d128ef · outbound

This paper cites Towards photo-realistic virtual try-on by adaptively generating-preserving image content.

Learning Flow Fields in Attention for Controllable Person Image Generation Towards photo-realistic virtual try-on by adaptively generating-preserving image content

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.534747Z

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Observation 80bdf8c1-d303-498c-9ac4-e95eed8486e7 · outbound

This paper cites Texture-preserving diffusion models for high-fidelity virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation Texture-preserving diffusion models for high-fidelity virtual try-on

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.512382Z

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Observation 8d740281-7f62-4ebc-9e0b-e8702edac479 · outbound

This paper cites D4-vton: Dynamic semantics disentangling for differential diffusion based virtual try-on.

Learning Flow Fields in Attention for Controllable Person Image Generation D4-vton: Dynamic semantics disentangling for differential diffusion based virtual try-on

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.492374Z

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Observation 1d5926e1-fb0a-47a4-9f22-d165064ec120 · outbound

This paper cites Cat-dm: Controllable accelerated virtual try-on with diffusion model.

Learning Flow Fields in Attention for Controllable Person Image Generation Cat-dm: Controllable accelerated virtual try-on with diffusion model

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.463818Z

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Observation 7b3ec165-817c-4f6e-8f9f-460b18480ef6 · outbound

This paper cites Pise: Person image synthesis and editing with decoupled gan.

Learning Flow Fields in Attention for Controllable Person Image Generation Pise: Person image synthesis and editing with decoupled gan

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.442949Z

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Observation ae6552c7-89fc-4fec-9b4d-ca603b56207e · outbound

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

Learning Flow Fields in Attention for Controllable Person Image Generation Adding conditional control to text-to-image diffusion models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.061407Z

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Observation 72b2273b-6c12-4a04-8938-9813e8deb6a1 · outbound

This paper cites Exploring dual-task correlation for pose guided person image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Exploring dual-task correlation for pose guided person image generation

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.413847Z

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Observation a7336b5e-0b2b-4ae1-841e-d3547df9f2bb · outbound

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

Learning Flow Fields in Attention for Controllable Person Image Generation The unreasonable effectiveness of deep features as a perceptual metric

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.071891Z

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Observation c6ed1657-b5f9-4766-b298-b8d07f6c5959 · outbound

This paper cites Dream: Diffusion rectification and estimation-adaptive models.

Learning Flow Fields in Attention for Controllable Person Image Generation Dream: Diffusion rectification and estimation-adaptive models

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.381216Z

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Observation 95067787-e141-4c90-856a-c5d42aa3859e · outbound

This paper cites Cocosnet v2: Full-resolution correspondence learning for image translation.

Learning Flow Fields in Attention for Controllable Person Image Generation Cocosnet v2: Full-resolution correspondence learning for image translation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.359644Z

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Observation eac5ee2b-9a65-4ff3-b454-02c91c964aa0 · outbound

This paper cites Cross attention based style distribution for controllable person image synthesis.

Learning Flow Fields in Attention for Controllable Person Image Generation Cross attention based style distribution for controllable person image synthesis

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.343912Z

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Observation 07a4ed3a-b82d-448c-854c-bdc23f1ae201 · outbound

This paper cites Tryondiffusion: A tale of two unets.

Learning Flow Fields in Attention for Controllable Person Image Generation Tryondiffusion: A tale of two unets

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T17:51:46.092889Z

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Observation f630138c-6492-409d-8a98-4f033555252e · outbound

This paper cites M&m vto: Multi-garment virtual try-on and editing.

Learning Flow Fields in Attention for Controllable Person Image Generation M&m vto: Multi-garment virtual try-on and editing

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.316049Z

Source-reported events for the cited work

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Observation 6d377b65-d280-472c-abb7-61a13f26b4a8 · outbound

This paper cites Progressive pose attention transfer for person image generation.

Learning Flow Fields in Attention for Controllable Person Image Generation Progressive pose attention transfer for person image generation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:51:46.293870Z

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Pith citing papers

Observation 40b86662-d54f-4b89-8f18-fbfc263bdfe1 · inbound

FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models cites this paper.

FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models Learning Flow Fields in Attention for Controllable Person Image Generation

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T16:47:47.426448Z

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Observation 801c7aff-95f1-492b-ac4e-72c829bdddb0 · inbound

Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items cites this paper.

Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items Learning Flow Fields in Attention for Controllable Person Image Generation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T12:26:11.543360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3753012d-95f0-4d98-b09f-b45a78540238 · inbound

Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items cites this paper.

Tstars-Tryon 1.0: Robust and Realistic Virtual Try-On for Diverse Fashion Items Learning Flow Fields in Attention for Controllable Person Image Generation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:51:29.279632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3732c706-06b4-48de-bc50-d6bcd44d37b1 · inbound

WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment cites this paper.

WearWow: Native 2K Multi-Garment Virtual Try-On via Adaptive Token Packing and Preference Alignment Learning Flow Fields in Attention for Controllable Person Image Generation

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-01T11:22:44.157732Z

Source-reported events for the cited work

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Observation 3a758785-f5d2-4a0e-b77f-959efb304498 · inbound

Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On cites this paper.

Oxygen-TryOn: Fashion-Native Foundation Model for Any-item Virtual Try-On Learning Flow Fields in Attention for Controllable Person Image Generation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T07:07:13.806326Z

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