Paper Citation Record · LEDGER

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models

As of 12 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2501.00651.

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

pith.paper-citation-record.v1
2501.00651 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:50:15.780491Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:40:25.683392Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T22:41:53.338995Z

Reference resolution

56 of 56 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 25672cf9-94ab-4e8e-bffa-28c608f06d1f · outbound

This paper cites Ren- derdiffusion: Image diffusion for 3d reconstruction, inpaint- ing and generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Ren- derdiffusion: Image diffusion for 3d reconstruction, inpaint- ing and generation

Reference 1

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Observation 85651e4e-11c7-465a-89ed-675ed129f142 · outbound

This paper cites Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Re-imagine the Negative Prompt Algorithm: Transform 2D Diffusion into 3D, alleviate Janus problem and Beyond

Reference 2

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Observation f76cefdf-25de-4d9d-ad53-4bee90d4b1c1 · outbound

This paper cites 4d-fy: Text-to-4d generation using hybrid score dis- tillation sampling.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models 4d-fy: Text-to-4d generation using hybrid score dis- tillation sampling

Reference 3

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Observation 243558da-ccad-4d9a-ba64-56443a944d9f · outbound

This paper cites Flux.1: An advanced generative ai model.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Flux.1: An advanced generative ai model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.245572Z

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Observation 31a5ec4a-ecb1-4f4f-9713-e371d51afec0 · outbound

This paper cites SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models SF3D: Stable Fast 3D Mesh Reconstruction with UV-unwrapping and Illumination Disentanglement

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.612865Z

Source-reported events for the cited work

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Observation 6a2a2d5b-77a9-40a1-815c-13116b35d93d · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.616551Z

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Observation 9ce7da3d-79c4-4a84-9955-1344f210b051 · outbound

This paper cites Large-Vocabulary 3D Diffusion Model with Transformer.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Large-Vocabulary 3D Diffusion Model with Transformer

Reference 7

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Observation a87553b3-e30a-4b0c-93a4-01abd81e88a0 · outbound

This paper cites pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.235092Z

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Observation 523c4be3-aceb-4206-ab6d-3b39ea8ba58e · outbound

This paper cites Efficient geometry-aware 3d generative adversarial networks.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Efficient geometry-aware 3d generative adversarial networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.225523Z

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Observation 21694a92-5d24-46a0-99bf-72629e837329 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models ShapeNet: An Information-Rich 3D Model Repository

Reference 10

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Observation 44a3db84-9037-403e-be53-2acf4eda6743 · outbound

This paper cites Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Single-stage diffusion nerf: A unified approach to 3d generation and reconstruction

Reference 11

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Observation 8ec7187d-55a0-4176-8a1c-2822c97745a6 · outbound

This paper cites Introducing auraflow v0.1, an open exploration of large rectified flow models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Introducing auraflow v0.1, an open exploration of large rectified flow models

Reference 12

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Observation 89711f81-3b03-4276-9deb-41e2df602332 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Objaverse: A universe of annotated 3d objects

Reference 13

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Observation ee0cd34f-188d-4e1c-8526-4b62671ecc15 · outbound

This paper cites Objaverse-xl: A universe of 10m+ 3d objects.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Objaverse-xl: A universe of 10m+ 3d objects

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.194340Z

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Observation 90fc6a0e-fe2f-44ca-814c-f6e33b87db45 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Diffusion models beat gans on image synthesis

Reference 15

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Observation 5c1ed764-9c8c-47c7-b9fd-9744236bec21 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.179111Z

Source-reported events for the cited work

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Observation 844f28b4-6e1b-409c-987b-ab4bf6a16e56 · outbound

This paper cites Get3d: A generative model of high quality 3d tex- tured shapes learned from images.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Get3d: A generative model of high quality 3d tex- tured shapes learned from images

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.168849Z

Source-reported events for the cited work

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Observation 35341aa5-02e0-4fad-a2f0-993673f542aa · outbound

This paper cites Generative adversarial nets.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Generative adversarial nets

Reference 18

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Observation 2cd10219-0f2b-4c69-b8bb-4c5733edc7b4 · outbound

This paper cites 3DGen: Triplane Latent Diffusion for Textured Mesh Generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models 3DGen: Triplane Latent Diffusion for Textured Mesh Generation

Reference 19

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Observation abd5e4d9-bf73-4f6f-b6a8-f6ec3efe0ff1 · outbound

This paper cites CLIPScore: a reference-free evaluation met- ric for image captioning.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models CLIPScore: a reference-free evaluation met- ric for image captioning

Reference 20

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Observation b50d93a9-e3ab-40e7-a06b-093c1bc9cee8 · outbound

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

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 21

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Source-reported events for the cited work

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Observation aeb6bee5-ede1-40d8-a491-c53c7939aefa · outbound

This paper cites Denoising dif- fusion probabilistic models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Denoising dif- fusion probabilistic models

Reference 22

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Observation 1d6fe817-5f9a-4e16-abba-18dcedd13928 · outbound

This paper cites 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models 3DTopia: Large Text-to-3D Generation Model with Hybrid Diffusion Priors

Reference 23

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Observation c95ed800-b905-4697-a312-6ee254a90bcf · outbound

This paper cites LRM: Large Reconstruction Model for Single Image to 3D.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models LRM: Large Reconstruction Model for Single Image to 3D

Reference 24

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Observation 34232508-0a68-47c2-900a-dd591b54e823 · outbound

This paper cites Pointinfinity: Resolution- invariant point diffusion models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Pointinfinity: Resolution- invariant point diffusion models

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.135146Z

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Observation b263a664-1f08-4905-8a7b-baeaca94baca · outbound

This paper cites Re- thinking fid: Towards a better evaluation metric for image generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Re- thinking fid: Towards a better evaluation metric for image generation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.125154Z

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Observation 258a4520-f0d3-4e21-9d84-decb7765defd · outbound

This paper cites Scal- ing up gans for text-to-image synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Scal- ing up gans for text-to-image synthesis

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.115378Z

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Observation 44fade7a-2034-4b8d-8ce1-e298d083d9e6 · outbound

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

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Elucidating the design space of diffusion-based generative models

Reference 28

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Observation 24bbae85-ec0a-4b2c-a5f3-c6230a22e7f0 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models 3d gaussian splatting for real-time radiance field rendering

Reference 29

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Observation f58a4c83-69b8-4066-846f-cf7fc426cc11 · outbound

This paper cites Auto-Encoding Variational Bayes.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Auto-Encoding Variational Bayes

Reference 30

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Observation 2068d467-4e19-4ae3-9385-3b34ae82dbba · outbound

This paper cites Improved precision and recall met- ric for assessing generative models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Improved precision and recall met- ric for assessing generative models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.093059Z

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Observation caff5aa2-045f-42e9-b756-79fcfb90457c · outbound

This paper cites Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Ln3diff: Scalable latent neural fields diffusion for speedy 3d generation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.082730Z

Source-reported events for the cited work

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Observation cffc2fdc-5de9-48a7-8d36-55d4079a81fe · outbound

This paper cites Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model

Reference 33

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Observation 482a9c40-1bf1-492d-89ee-f0bd4250b8dd · outbound

This paper cites Direct-3d: Learning direct text-to-3d generation on massive noisy 3d data.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Direct-3d: Learning direct text-to-3d generation on massive noisy 3d data

Reference 34

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Observation 60a7db41-245a-41d5-a0fc-c13455bab1cb · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 35

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Observation e600c53d-c945-414c-ad92-5d59ccb918d4 · outbound

This paper cites Decoupled Weight Decay Regularization.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Decoupled Weight Decay Regularization

Reference 36

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Observation 1a72f90a-4e0b-4d8d-8912-ab51bafd7d82 · outbound

This paper cites Scalable 3d captioning with pretrained models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Scalable 3d captioning with pretrained models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.060881Z

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Observation 6a55dfd9-2916-4b8d-b954-a8e67ee6f66d · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 38

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Observation 24d187a9-2bbd-4871-9949-52359599782e · outbound

This paper cites Diffrf: Rendering-guided 3d radiance field diffusion.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Diffrf: Rendering-guided 3d radiance field diffusion

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.044827Z

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Observation fcab4e50-2ed3-4161-8878-c6279ea74c9c · outbound

This paper cites Stylesdf: High-resolution 3d-consistent image and geome- try generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Stylesdf: High-resolution 3d-consistent image and geome- try generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.034583Z

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Observation 20e7f2c2-8805-47c0-91d8-a5af939ecdd2 · outbound

This paper cites Benchmark for compositional text- to-image synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Benchmark for compositional text- to-image synthesis

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:16.025066Z

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Observation d8bd708e-155b-4c02-b6e8-e94699b83f04 · outbound

This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Deepsdf: Learning con- tinuous signed distance functions for shape representation

Reference 42

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Observation 7cf4afd7-0646-429f-8069-8505b14d6597 · outbound

This paper cites Scalable diffusion models with transformers.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Scalable diffusion models with transformers

Reference 43

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Observation 546bffcb-100e-4932-b63a-2098bf62184c · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models DreamFusion: Text-to-3D using 2D Diffusion

Reference 44

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Source-reported events for the cited work

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Observation 6ed90503-dcf6-4399-aa58-69ece8c23e3a · outbound

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

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Learning transferable visual models from natural language supervision

Reference 45

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Source-reported events for the cited work

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Observation f6936c2f-73e4-4753-90fa-22c0673979a2 · outbound

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

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models High-resolution image synthesis with latent diffusion models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.746272Z

Source-reported events for the cited work

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Observation 8e796fb0-4bff-4f1c-a29f-3f71e8de7af6 · outbound

This paper cites Graf: Generative radiance fields for 3d-aware im- age synthesis.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Graf: Generative radiance fields for 3d-aware im- age synthesis

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.749580Z

Source-reported events for the cited work

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Observation bc2db109-e9f5-4563-bcd3-3829efd3b761 · outbound

This paper cites 3d neural field generation using triplane diffusion.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models 3d neural field generation using triplane diffusion

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.753041Z

Source-reported events for the cited work

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Observation 8db18f6f-c4dc-4f22-8e1e-8f382265d7b8 · outbound

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

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.756280Z

Source-reported events for the cited work

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Observation 93f89432-cccb-4e7c-86a2-794aaaadca26 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Roformer: Enhanced transformer with rotary position embedding

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.759918Z

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Observation 86ad4ad6-ca97-4abd-93a2-a20cd91c2bb2 · outbound

This paper cites TripoSR: Fast 3D Object Reconstruction from a Single Image.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models TripoSR: Fast 3D Object Reconstruction from a Single Image

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:15.763335Z

Source-reported events for the cited work

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Observation 1e3d5428-b80e-4f77-bd2a-ea45e5bf7ee4 · outbound

This paper cites Lion: Latent point dif- fusion models for 3d shape generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Lion: Latent point dif- fusion models for 3d shape generation

Reference 52

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Observation 76a8b763-af87-495c-bb2f-5fbd77820171 · outbound

This paper cites Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation

Reference 53

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Observation f479a4a2-8ab1-4767-98da-56f32f15c60b · outbound

This paper cites Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distilla- tion

Reference 54

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Observation a2cfb039-a438-4f2e-810b-96691690ff8d · outbound

This paper cites InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Reference 55

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Observation 3c354183-14b6-4227-9663-34389519790c · outbound

This paper cites a pair of comfortable blue jeans.

Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models a pair of comfortable blue jeans

Reference 56

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T22:50:15.955626Z

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Pith citing papers

Observation 435df0b3-f7aa-42c6-b6ef-26ebf344cc73 · inbound

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation cites this paper.

A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models

Reference 205

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Observation bc8c5afc-7198-4d00-a52e-b6837e6a3814 · inbound

Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework cites this paper.

Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework Taming Feed-forward Reconstruction Models as Latent Encoders for 3D Generative Models

Reference 89

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