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Paper Citation Record · LEDGER

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

As of 9 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 2 inbound Pith citation observations for arXiv:2508.20470.

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

pith.paper-citation-record.v1
2508.20470 v1

Coverage vector

measured 79 of 79 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:10:34.667840Z

measured 81 of 81 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-12T07:34:02.473153Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-09T22:54:16.471407Z

Reference resolution

79 of 79 outbound references displayed

  • verified exact3
  • verified fuzzy27
  • unresolved49
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cdb88eb0-d696-42d4-8975-d7f79a52a4a1 · outbound

This paper cites Cogvideox-fun.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Cogvideox-fun

Reference 1

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verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.477185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.309181Z digest=sha256:24926292ec995b23e96d785797f14719bc82cb6ccf72056f203528a4991e14a5

Observation 038ae0e0-fb70-4c88-b119-cc85144b74cb · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 2

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source=pdf_text observed=2026-08-05T15:10:34.412028Z digest=sha256:8dbce556b26a10eee6811bb62acb7fcf3f38dc0224dc8b5e31aee033413d8cc8

Observation 15a7a203-0b00-42e4-9124-10300e28d5b0 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation ShapeNet: An Information-Rich 3D Model Repository

Reference 3

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source=pdf_text observed=2026-08-05T15:10:34.416626Z digest=sha256:343f2163a674c7ce172ebd174147f9b16beffa6057b31d6807c7c0e47155721c

Observation df4cb201-3849-473c-ab08-4c7d8da7ce65 · outbound

This paper cites V3D: Video Diffusion Models are Effective 3D Generators.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation V3D: Video Diffusion Models are Effective 3D Generators

Reference 4

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source=pdf_text observed=2026-08-05T15:10:34.420320Z digest=sha256:53e7580c13d10204d2e7dbb7f586409aa6be9b370732ab97c3980b9513abfb0f

Observation 4aedacee-1980-4c7c-917e-1a2627eb6f77 · outbound

This paper cites LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes

Reference 5

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source=pdf_text observed=2026-08-05T15:10:34.423991Z digest=sha256:33a795bdca265f65eeaabc5b10daea3ec39cea307e1decf6964bb3d84104badf

Observation eb9a176c-7e88-48d8-a660-86665ff3f2ac · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Objaverse-xl: A universe of 10m+ 3d objects

Reference 7

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source=pdf_text observed=2026-08-05T15:10:34.431666Z digest=sha256:ae78be95463f11be119f820ad8f75c11229c35952a0cf0864c22680a1a315eaa

Observation d06bf7e9-5878-410d-b685-315402876487 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Objaverse: A universe of annotated 3d objects

Reference 8

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source=pdf_text observed=2026-08-05T15:10:34.434540Z digest=sha256:134b8fd7d590a0eb20718a92414081a81f97d9d16ba75e1d51bac938a65d30b5

Observation 1c6ece97-f254-4715-961b-dbf1ba133a08 · outbound

This paper cites Anymate: A dataset and baselines for learning 3d object rigging.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Anymate: A dataset and baselines for learning 3d object rigging

Reference 9

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raw_fallback, observed 2026-08-05T15:10:35.454425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.437668Z digest=sha256:489a08e83d90cd3c02dc7ab8e74acb21657d6ff8047fa13c55cf6a569dad708e

Observation 19effd8d-d214-47d5-95bf-bc6f0dd197a3 · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation 8-bit Optimizers via Block-wise Quantization

Reference 10

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source=pdf_text observed=2026-08-05T15:10:34.440764Z digest=sha256:3a4a8abde9d22d35239236e2028b4fcdd88d8e05099637c1c7db58669a3b7290

Observation 455e98bc-a6fa-44bc-898e-bb2f1b00653b · outbound

This paper cites Google scanned objects: A high-quality dataset of 3d scanned household items.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Google scanned objects: A high-quality dataset of 3d scanned household items

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.444466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.444251Z digest=sha256:2df7fd1cc46773b21edf2f5844e231e1d50e2a569e3734f6f3d68c89932ed420

Observation 76c09b22-09ef-4482-a256-c8677765ca0e · outbound

This paper cites Scaling Rectified Flow Transformers for High-Resolution Image Synthesis.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Reference 12

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source=pdf_text observed=2026-08-05T15:10:34.447593Z digest=sha256:5d2efe7adb3f57769579a4669b0de19f83844867fa147a34d9f651231c99fc80

Observation 17e3b382-9ebc-4316-925c-107e4111cbd6 · outbound

This paper cites Video-R1: Reinforcing Video Reasoning in MLLMs.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Video-R1: Reinforcing Video Reasoning in MLLMs

Reference 13

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source=pdf_text observed=2026-08-05T15:10:34.450897Z digest=sha256:0538d224b099b917b889a0a62474304e33d31cc92f27a3a032cc968ef30b2a3b

Observation 00056075-ff47-423b-9e10-f36030e75984 · outbound

This paper cites 3d-future: 3d furniture shape with texture.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation 3d-future: 3d furniture shape with texture

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.434841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.453727Z digest=sha256:e9f9bf8c1acc3e6dd24d24c9d57a2a59a5c11cf1566de84703314c416390a57f

Observation facc6d13-c6f4-4733-8839-c90479ad228d · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 15

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source=pdf_text observed=2026-08-05T15:10:34.457032Z digest=sha256:a1995d58d438653dbaa2d2a704ca0af868be797397bafd338d4105b9ce0358ed

Observation 3f65ac03-c715-434a-a339-3e3c1ed85d77 · outbound

This paper cites Vfusion3d: Learning scalable 3d generative models from video diffusion models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Vfusion3d: Learning scalable 3d generative models from video diffusion models

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.425434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.460076Z digest=sha256:60d204ed828d2afdb4a299e3d1c0e30dff92e911105f36298e0732f549ee4cb7

Observation fd0bcfaa-ced7-4c8a-a4bb-784139fc100b · outbound

This paper cites MVImgNet2.0: A Larger-scale Dataset of Multi-view Images.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation MVImgNet2.0: A Larger-scale Dataset of Multi-view Images

Reference 17

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source=pdf_text observed=2026-08-05T15:10:34.464265Z digest=sha256:b8aeda0a5733543dddd614fb7ab28de30c4e6cebee918eb1c72da1f21f82e59b

Observation d958bc0b-8f21-4635-a707-b99e7de1bffb · outbound

This paper cites CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

Reference 18

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source=pdf_text observed=2026-08-05T15:10:34.467803Z digest=sha256:752eb933b237296ece79355c2285eff80687bfae31e88b8557db17889695f64a

Observation 48f5ad7a-0b13-48d9-a84c-74fe91651b8e · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation LRM: Large Reconstruction Model for Single Image to 3D

Reference 19

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source=pdf_text observed=2026-08-05T15:10:34.470949Z digest=sha256:c11f963b72d95f6c8026563627e85633c70c6fafda8d0f75b1d92f7497b7d13f

Observation 21de0803-f76f-41a1-883c-72c15bd8c000 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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source=pdf_text observed=2026-08-05T15:10:34.474148Z digest=sha256:c724f4215c2e888976259497d74f52c2cc6d3437bbad2b75273174810c11ac0d

Observation 308b1e13-b853-45d9-8cde-43f304a4edae · outbound

This paper cites Edit360: 2D Image Edits to 3D Assets from Any Angle.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Edit360: 2D Image Edits to 3D Assets from Any Angle

Reference 21

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source=pdf_text observed=2026-08-05T15:10:34.477296Z digest=sha256:3af4d3f3daba9b62f9430786c3495e0da522a9f6a5cd7012004078d163646d5b

Observation 08b9e0f6-d422-4e49-abe8-425f98cd375d · outbound

This paper cites Epidiff: Enhancing multi-view synthesis via localized epipolar-constrained diffusion.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Epidiff: Enhancing multi-view synthesis via localized epipolar-constrained diffusion

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.415474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.480357Z digest=sha256:c918ac2b05d117dd244b13ce3d3e3eada7fca616f63d4eed51102348d90d17b8

Observation 920d776e-ef5c-4ce2-bce1-0c264c059269 · outbound

This paper cites Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

Reference 23

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source=pdf_text observed=2026-08-05T15:10:34.483179Z digest=sha256:4bcc71a7d07b1f6155e3aa90915111277ea78e1a8ea2b6d0c2a8babf07e66a73

Observation 988279af-96ff-4ed8-bd96-30bc857d3558 · outbound

This paper cites OpenAI o1 System Card.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation OpenAI o1 System Card

Reference 24

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source=pdf_text observed=2026-08-05T15:10:34.486525Z digest=sha256:c4a2a63cad71e0fb7b599808b5fc123b073e7267125025a6d6622a242ce279cd

Observation 17b7d81f-4e50-41bc-83c2-e5a8a534ae52 · outbound

This paper cites Animate3D: Animating Any 3D Model with Multi-view Video Diffusion.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Animate3D: Animating Any 3D Model with Multi-view Video Diffusion

Reference 25

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source=pdf_text observed=2026-08-05T15:10:34.489621Z digest=sha256:3dc0e0ffa8c1bbff6b8a1e1ccf7060790ba4bf5eb9c62a01514869c053512fdf

Observation ad06960e-1424-4cd1-93aa-5ac15bedcb22 · outbound

This paper cites Shap-E: Generating Conditional 3D Implicit Functions.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Shap-E: Generating Conditional 3D Implicit Functions

Reference 26

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source=pdf_text observed=2026-08-05T15:10:34.492842Z digest=sha256:542520d89b8ca31aeedef577425af7036643435ea5c8fece4c91387e7870242f

Observation 695a08c6-6853-4d49-9691-7fb91f2bc1f2 · outbound

This paper cites Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models

Reference 27

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verified exact
local_arxiv, observed 2026-08-05T15:10:34.993723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.496187Z digest=sha256:a25de0617f7a00711b0a68f3992f44f3afc2f9fb40bf2760a0e0b9e84e9548ad

Observation 4cfde178-041e-44a6-837a-b1fd290b6443 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation 3d gaussian splatting for real-time radiance field rendering

Reference 28

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source=pdf_text observed=2026-08-05T15:10:34.499286Z digest=sha256:fdf99bc8b0ea3df951acfe547d9b4c7f70d4149ba03f1a097c580d1d5f6dd065

Observation d4ac9a47-1d0b-4352-b197-5e3f16c30e7e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Adam: A Method for Stochastic Optimization

Reference 29

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source=pdf_text observed=2026-08-05T15:10:34.502295Z digest=sha256:b277570e5bc0bcaee238cbca3cb476480c3c6cb1fa5518cc6326b385883e3f67

Observation f9a65250-3c3d-4b5f-b1e4-426cdf3a4671 · outbound

This paper cites FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space

Reference 30

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source=pdf_text observed=2026-08-05T15:10:34.505566Z digest=sha256:f54eb802955a678714b59715f1f9336ed805397e6abd75ef8cb8057099b39f26

Observation 386283b7-d241-47bf-846f-3626931ae5b2 · outbound

This paper cites Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details

Reference 31

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source=pdf_text observed=2026-08-05T15:10:34.508756Z digest=sha256:1121ab89eb8dda5c151608cc4a2b31b57fa0deb1f76432447fde605935ef27bd

Observation 04c95281-7e4f-4149-aeba-d220a21ac823 · outbound

This paper cites Image content generation with causal reasoning.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Image content generation with causal reasoning

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.400254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.512022Z digest=sha256:6b323a4ae632ad4696368a6e63cc9d4a4326bdb739a8b21ef3f9c2b01e646c82

Observation 37e92540-c9fd-4f8c-bdcd-5b5e6de8ae4a · outbound

This paper cites Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Controllable Text-to-3D Generation via Surface-Aligned Gaussian Splatting

Reference 33

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source=pdf_text observed=2026-08-05T15:10:34.514985Z digest=sha256:2867268386cc2959182dbe30a521466a8f4a0f055222103c56ff5d8c86aed04f

Observation 193df7d9-d4fa-4e5b-a63f-55daa513e363 · outbound

This paper cites Magic3d: High-resolution text-to-3d content creation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Magic3d: High-resolution text-to-3d content creation

Reference 34

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source=pdf_text observed=2026-08-05T15:10:34.518286Z digest=sha256:a93ad719bef929519cb342941522d79c84afbd9b257fdfe85c5f20ec71de2b57

Observation 182183d9-301d-476e-9043-ed6357f67143 · outbound

This paper cites Objaverse++: Curated 3D Object Dataset with Quality Annotations.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Objaverse++: Curated 3D Object Dataset with Quality Annotations

Reference 35

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source=pdf_text observed=2026-08-05T15:10:34.521155Z digest=sha256:a5d8888e8202d0d6822e89a2bdbb1ffcaa48e0475914860c03ee1b5556483188

Observation 4b1e83dd-5bb9-4be1-a556-b26f2b2eb825 · outbound

This paper cites Kiss3dgen: Repurposing image diffusion models for 3d asset generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Kiss3dgen: Repurposing image diffusion models for 3d asset generation

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.384411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.524181Z digest=sha256:bc6c306bb7ff3ab126547287158536a7dfc9ee518186fcaf085de609ef277358

Observation 6d3fd8a9-021a-48bf-a016-196e2d05a961 · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Zero-1-to-3: Zero-shot one image to 3d object

Reference 37

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raw_fallback, observed 2026-08-05T15:10:35.374770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.527093Z digest=sha256:e8b95eedcf3ea6d1a6765749a6847ffb88cea03f15d1acb38c1219a636e4f43b

Observation 57671bae-21e7-4d06-ac69-bcadccea9287 · outbound

This paper cites Uncommon objects in 3d.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Uncommon objects in 3d

Reference 38

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raw_fallback, observed 2026-08-05T15:10:35.363331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.530034Z digest=sha256:aa124ec4942ac6bf4b4255a294080f38aac6d79e201c8ecf334f237d5ac8cf09

Observation 06297104-cd06-42d9-bd7d-e1a06b61ab23 · outbound

This paper cites Orientation matters: Making 3d generative models orientation-aligned.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Orientation matters: Making 3d generative models orientation-aligned

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.533584Z digest=sha256:31b29850da7656e25240509cab2790561d4c18f4161d2d127f63ad978eda363f

Observation 068be735-d772-42cb-9b09-7a8786d46ca6 · outbound

This paper cites Scalable 3d captioning with pretrained models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Scalable 3d captioning with pretrained models

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.353318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.537766Z digest=sha256:4587ebd111df7dfe5a7c5c29a225e359410847a17727ae66b7919c1750cae2cf

Observation 4fd6106c-ab44-4a7b-b202-50eebb46bc4e · outbound

This paper cites IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.540672Z digest=sha256:565a564ff442b03f9d82b69a79183d2a84ca136e0f4936220eb5c51a261c96bc

Observation f7a635c1-9b25-4986-9264-c6ab6706f954 · outbound

This paper cites Point-E: A System for Generating 3D Point Clouds from Complex Prompts.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Point-E: A System for Generating 3D Point Clouds from Complex Prompts

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.544067Z digest=sha256:54f56ed74ad4c29d1e84139693e65a1cb36143b360da51fcb942793be4a09576

Observation 68c2a97a-ea46-4811-a9be-7b9acc7eb3a7 · outbound

This paper cites Gpt-4 technical report, 2023.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Gpt-4 technical report, 2023

Reference 43

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no resolver link, observed 2026-08-05T15:10:34.548134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.548134Z digest=sha256:4a44bb4190ea684c12541f36f93b5284cbeb73a4b9bd4b6452c95389a0989f29

Observation 30e97978-bb67-4159-93ea-fb86f10062cf · outbound

This paper cites Openai-sora.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Openai-sora

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.337399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.551028Z digest=sha256:6747d9fb06186693b808e6b38534a856763fddfdec79c3cfd4156b8d8d7a3df7

Observation 4c2527ef-cc38-4efc-ad70-ff8fbcc94799 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation DreamFusion: Text-to-3D using 2D Diffusion

Reference 45

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no resolver link, observed 2026-08-05T15:10:34.554105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.554105Z digest=sha256:645dc8bf497f3e07da692a88d898b08fc3ef3a7a0d5dd12e1cce77502c08ec41

Observation 584d06d6-33dd-4a29-952f-11021f0e644a · outbound

This paper cites Improving language understanding by generative pre-training.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Improving language understanding by generative pre-training

Reference 46

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no resolver link, observed 2026-08-05T15:10:34.557396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.557396Z digest=sha256:1d45066c36b2aaf5e473604b692d8f7981a8d8402b3bfc0ae516f06db72e915f

Observation 472030f9-055a-44b2-80a1-1fd0184db3c0 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.560555Z digest=sha256:22c1f0e4a78e0b6d959e7883b3e0cbcd8e8f519589e035391be62440da247936

Observation 871b4c6f-c493-4906-a033-f64f1d81060d · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 48

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no resolver link, observed 2026-08-05T15:10:34.563508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.563508Z digest=sha256:c6b05ae06f19f5ae7a4f1ea6cb4add0e120461a3cd5aa3f7ec41cc7f165344e5

Observation 70c8a25c-28ef-4056-8ef8-35b278d2d0ee · outbound

This paper cites Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.310568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.566498Z digest=sha256:dbe8936988bea070eb598a58ee4680f18ed54a7d5770816bc1509bf68415eeb3

Observation 3f609a49-d823-455e-afb3-2716c9b87070 · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation High- resolution image synthesis with latent diffusion models

Reference 50

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no resolver link, observed 2026-08-05T15:10:34.570120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.570120Z digest=sha256:b30274774cf3be583ecac8118c2d01a32d5a2300ba5e081a1ea550b8bc277ad9

Observation 351da0f7-503f-485e-8c73-6e984f5f1a3f · outbound

This paper cites Laion- 5b: An open large-scale dataset for training next generation image-text models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Laion- 5b: An open large-scale dataset for training next generation image-text models

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.573050Z digest=sha256:3c7673a07b17903966aaf5563a8d5611b16720cd8099f3e1eb64fa502249d3dc

Observation 7a96b04b-eb18-431a-a158-6f5ffbf3a8e9 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.575950Z digest=sha256:081abdad1b7860b45856fbefc41db3a41281ba4ecb36f2043738910c9913d8f8

Observation 64f4bad2-df1b-4476-9751-a4f2260e6bc2 · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 53

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no resolver link, observed 2026-08-05T15:10:34.579065Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.579065Z digest=sha256:7df76f14535f6fffe4929b0f8d5a31b1f1c4ddcf04ab4541963af4305d4dc27d

Observation b7d59c6b-e639-44f8-b18b-750f6f83a37f · outbound

This paper cites Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.582125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.582125Z digest=sha256:a074cc9e251def7d753c86685b5629565ab2cd76670f30b2bb6d7286d58675fb

Observation ee4f77f1-eedc-47eb-b304-ce37991a084d · outbound

This paper cites MVDream: Multi-view Diffusion for 3D Generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation MVDream: Multi-view Diffusion for 3D Generation

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.585377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.585377Z digest=sha256:215b81290f658a7ac48c6ada37b872e5795d8078d8fa186c317e950e46918564

Observation f9652382-de4c-49bb-bc02-485c0500120e · outbound

This paper cites Lgm: Large multi-view gaussian model for high-resolution 3d content creation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Lgm: Large multi-view gaussian model for high-resolution 3d content creation

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.288855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.588600Z digest=sha256:a2b7a5649112567e3780f7130570e0ca1351a3bb1e79ae4879a2f2c8489d72d6

Observation 41ea7506-0a8d-4a72-8eaa-8d8d530f60d0 · outbound

This paper cites Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.278939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.592179Z digest=sha256:874b1e9df355bbdbac61368e5dba22d125abaca17214638f847c6e982268e97f

Observation b72c936c-abca-49f0-a910-a9fd905b1e37 · outbound

This paper cites Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Sv3d: Novel multi-view synthesis and 3d generation from a single image using latent video diffusion

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.269035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.595343Z digest=sha256:f29efc17987899e6c27f8280cad86307f42a69e373dab67e51fbcce5659edbaa

Observation 093cb559-b945-42fc-b056-fa5b197f6301 · outbound

This paper cites Nova-3d: Non-overlapped views for 3d anime character reconstruction.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Nova-3d: Non-overlapped views for 3d anime character reconstruction

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.258446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.598543Z digest=sha256:47943982ee49600df9b8bf0d710de6973bdb4dc7fd3e8e8c9ed181677c7b3737

Observation 6959c736-ee2a-4365-8e89-c65c617e1f14 · outbound

This paper cites Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Pro- lificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation

Reference 60

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.601674Z digest=sha256:b99a8b62acc72c49619dedcba19ca3c2a37307d29d1ac0473bbe791e18516ab7

Observation 64471e07-d3fa-4c1a-b259-a5e039e1784a · outbound

This paper cites Exploring video quality assessment on user generated contents from aesthetic and technical perspectives.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Exploring video quality assessment on user generated contents from aesthetic and technical perspectives

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.243200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.604647Z digest=sha256:04c8f6a0ab65f58f4396c4c2d2314356c272525b44fb4d7a7a7013902bc8ff2a

Observation ef461b07-f65d-4802-8c3e-5446b6d96f1c · outbound

This paper cites Genfusion: Closing the loop between reconstruction and generation via videos.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Genfusion: Closing the loop between reconstruction and generation via videos

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.233771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.607633Z digest=sha256:7896c42694b338eacaf900f0bf582587f4d6f979faa785badeeb7a2d37b3a88b

Observation 26693e0f-d6b0-4c3d-bc0c-6d5cee6d95c3 · outbound

This paper cites Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.223600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.610625Z digest=sha256:1b3a9227a0a02e79811f0a32d4a3be1931c78964f135222571bb44ec5f3b8dc1

Observation 455b0228-c763-4f03-8f8a-606d31342023 · outbound

This paper cites Structured 3d latents for scalable and versatile 3d generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Structured 3d latents for scalable and versatile 3d generation

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.613644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.613644Z digest=sha256:4eb1e740e99f8ada4bed90c5ec64209db2a891e31c52b51060dba10c35c1bb7e

Observation b792d6e1-8609-47a8-9a36-971bb6d5872d · outbound

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

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models

Reference 65

Resolution
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no resolver link, observed 2026-08-05T15:10:34.616483Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.616483Z digest=sha256:c9ef3d7331edd075af2daa2eef37395aa1953f5583ef04982ab5be4c1f96dc91

Observation 785e4a25-fc60-4d4b-8cc4-21b1e0221b68 · outbound

This paper cites FlexGen: Flexible Multi-View Generation from Text and Image Inputs.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation FlexGen: Flexible Multi-View Generation from Text and Image Inputs

Reference 66

Resolution
verified exact
local_arxiv, observed 2026-08-05T15:10:34.767528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.619929Z digest=sha256:87921c63fe0e0a7bda4c4abebe7f8444744d93ab4512bb83b01293e010f909fc

Observation 4f1081ed-3803-4694-8780-5070289dcb38 · outbound

This paper cites DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model

Reference 67

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no resolver link, observed 2026-08-05T15:10:34.623059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.623059Z digest=sha256:11299db710b564a8bcbeb3a46bc1ea7be38c2cba84c3de265f2d98d62908f17b

Observation e8dea98a-648f-4610-88d4-843f0babb429 · outbound

This paper cites Hi3d: Pursuing high-resolution image-to-3d generation with video diffusion models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Hi3d: Pursuing high-resolution image-to-3d generation with video diffusion models

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.207733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.626409Z digest=sha256:b927fcf3df8e9f3db0fa5df98085f406fa1c2df87a18a3d097e47e54b4987dc5

Observation 81c6d741-5c9d-4bb7-bb68-6749f00d3e1a · outbound

This paper cites Tv-3dg: Mastering text-to-3d customized generation with visual prompt.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Tv-3dg: Mastering text-to-3d customized generation with visual prompt

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.197341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.629383Z digest=sha256:581c0c76611122d7a23e6ac16136c7fd75e5fe79d725357e6e56f0a9afdc0403

Observation aa02705c-da16-400b-a985-9d492efe3d51 · outbound

This paper cites CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Reference 70

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unresolved
no resolver link, observed 2026-08-05T15:10:34.633085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.633085Z digest=sha256:14b1e2326da4a52efe9781aebcc5aeb8a76f879990fe005430b7b887731b8d5f

Observation 3a210bbb-1e9b-4f63-97d0-625b52f6cb0c · outbound

This paper cites Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Gaussiandreamer: Fast generation from text to 3d gaussians by bridging 2d and 3d diffusion models

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.186912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.636465Z digest=sha256:8e7280c1db06a5aa2918dee1fd476e0bfd3d93ada6aeeddc7edf3eaf5e084d6e

Observation 586b033e-6dc0-4acf-9023-138fc11c1652 · outbound

This paper cites GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation GaussianDreamerPro: Text to Manipulable 3D Gaussians with Highly Enhanced Quality

Reference 72

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unresolved
no resolver link, observed 2026-08-05T15:10:34.639509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.639509Z digest=sha256:ce14e88ebc8ac804bedead75df3dad6ce503055d5e9eb34f2e38e0ce889828df

Observation 4c9b92d3-4e7b-43cc-87e8-b059b70070ac · outbound

This paper cites Mvimgnet: A large-scale dataset of multi-view images.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Mvimgnet: A large-scale dataset of multi-view images

Reference 73

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b4b69543-bd70-499e-abfc-27f0ed1cc7ce · outbound

This paper cites DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal Consistent Video Generation

Reference 74

Resolution
verified exact
local_arxiv, observed 2026-08-05T15:10:34.728196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 5fb8b89d-8eb5-499e-b2e5-71b6ca52e8ca · outbound

This paper cites TexVerse: A Universe of 3D Objects with High-Resolution Textures.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation TexVerse: A Universe of 3D Objects with High-Resolution Textures

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.650803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.650803Z digest=sha256:1a62a5d909582b1d49b36aa5fcc40da4a0b9dbbafbfa939a8030fb3f56f3b6b8

Observation 954546e9-d23e-49df-9040-aef1526cf96f · outbound

This paper cites Mmvu: Measuring expert-level multi-discipline video understanding.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Mmvu: Measuring expert-level multi-discipline video understanding

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.167469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.654217Z digest=sha256:1c1f4c02f7eb838e2cbb8e4911d6e2ae94e6312eb9b8ad23e3a0e89d564d81a4

Observation d36a4827-8162-4144-b540-e1231c7ca298 · outbound

This paper cites Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.657279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.657279Z digest=sha256:be5f0dc03ff73fa0bd049177d26b59a38af982ab9fbc450877a24578842faad9

Observation 43889be4-aa2e-41fb-a759-57cf84690865 · outbound

This paper cites Thingi10K: A Dataset of 10,000 3D-Printing Models.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Thingi10K: A Dataset of 10,000 3D-Printing Models

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-05T15:10:34.660555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:10:34.660555Z digest=sha256:93d3be3fb072629100cced8ea7e91a3cbd81e7ce2268f7f37a8f3f9ea5bf945b

Observation 1038a5d0-fd50-4711-811b-14545ba6f2b2 · outbound

This paper cites High-fidelity 3d textured shapes generation by sparse encoding and adversarial decoding.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation High-fidelity 3d textured shapes generation by sparse encoding and adversarial decoding

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.157582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.663959Z digest=sha256:e58118cb6bb9168c039a7ac8928c07f1b29a1e7e6905d7638115d875023ddcd4

Observation c0da1c71-e5f0-444e-aac7-d6b1a7c0ecb6 · outbound

This paper cites Videomv: Consistent multi-view generation based on large video generative model.

Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation Videomv: Consistent multi-view generation based on large video generative model

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T15:10:35.147888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-05T15:10:34.667840Z digest=sha256:b5fb1113c1baf8e3c021a02bc1355d36dd71d1edab58be1f3236cf488e47ebd1

Pith citing papers

Observation 634c3c39-3fa9-430c-9b76-fe92a0b5a2eb · inbound

Animator-Centric Skeleton Generation on Objects with Fine-Grained Details cites this paper.

Animator-Centric Skeleton Generation on Objects with Fine-Grained Details Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-09T22:54:16.472675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T22:50:07.371117Z digest=sha256:25ecaa45e7202c005620ec76e993b4a0d107f1e2e46d3b9483db19592fde9405

Observation 0d5b7bb3-cdb4-416f-85e3-ffbda26acdbe · inbound

Global Pose Control for Generative View Synthesis in Normalized Object Coordinate Space cites this paper.

Global Pose Control for Generative View Synthesis in Normalized Object Coordinate Space Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-07-12T07:34:02.473153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T07:34:02.473153Z digest=sha256:7f484aa239e42ce65cb41dd3e5d119ae136fcf5b53feac2658c91cfa47bfa5b3