Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation | Paper Citation Record

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This paper cites Clipface: Text-guided editing of textured 3d mor- phable models.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Clipface: Text-guided editing of textured 3d mor- phable models

Reference 1

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This paper cites Bergman, Petr Kellnhofer, Yifan Wang, Eric R.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Bergman, Petr Kellnhofer, Yifan Wang, Eric R

Reference 2

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This paper cites Demystifying MMD GANs.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Demystifying MMD GANs

Reference 3

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This paper cites Text and image guided 3d avatar generation and ma- nipulation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Text and image guided 3d avatar generation and ma- nipulation

Reference 4

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Observation 7a8249a7-fc52-4ccb-9b14-8941913789a8 · outbound

This paper cites Chan, Connor Z.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Chan, Connor Z

Reference 5

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Observation 0658603d-2022-4351-8dae-9c5b743717c6 · outbound

This paper cites Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution

Reference 6

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This paper cites Generalizable and Animatable Gaussian Head Avatar.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Generalizable and Animatable Gaussian Head Avatar

Reference 7

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This paper cites Gen- erative adversarial networks: An overview.IEEE signal processing magazine, 35(1):53–65, 2018.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Gen- erative adversarial networks: An overview.IEEE signal processing magazine, 35(1):53–65, 2018

Reference 8

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Observation 86fc0d01-1ae1-420b-b519-1d0a7d9e4b19 · outbound

This paper cites Cogview: Mastering text-to-image generation via transformers.Advances in Neural Information Processing Systems, 34:19822–19835, 2021.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Cogview: Mastering text-to-image generation via transformers.Advances in Neural Information Processing Systems, 34:19822–19835, 2021

Reference 9

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This paper cites Cogview2: Faster and better text-to-image generation via hierarchical transformers.Advances in Neural Information Processing Systems, 35:16890–16902, 2022.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Cogview2: Faster and better text-to-image generation via hierarchical transformers.Advances in Neural Information Processing Systems, 35:16890–16902, 2022

Reference 10

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This paper cites Semantic image synthesis via adversarial learning.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Semantic image synthesis via adversarial learning

Reference 11

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This paper cites Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis.Advances in neural information processing systems, 34:3518–3532, 2021.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Imagebart: Bidirectional context with multinomial diffusion for autoregressive image synthesis.Advances in neural information processing systems, 34:3518–3532, 2021

Reference 12

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This paper cites Black, and Timo Bolkart.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Black, and Timo Bolkart

Reference 13

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Observation d2c986d5-db8e-4ecb-8199-9fb53d6fa1dc · outbound

This paper cites Generative adversarial nets.Advances in neural information processing systems, 27, 2014.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Generative adversarial nets.Advances in neural information processing systems, 27, 2014

Reference 14

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Observation ffe053af-9ceb-4890-b3b8-97d97d978c03 · outbound

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

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 15

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This paper cites Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Denoising diffu- sion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 16

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Observation 0241f0a9-ca3a-4eeb-8a02-2e28c7d011f0 · outbound

This paper cites Removing the quality tax in controllable face gener- ation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Removing the quality tax in controllable face gener- ation

Reference 17

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This paper cites GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation GSGAN: Adversarial Learning for Hierarchical Generation of 3D Gaussian Splats

Reference 18

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Observation 880bfad9-a36c-43dc-90ad-c07c88529523 · outbound

This paper cites ClipMatrix: Text-controlled Creation of 3D Textured Meshes.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation ClipMatrix: Text-controlled Creation of 3D Textured Meshes

Reference 19

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Observation 3f66b315-86c1-4ce8-b2db-ff6ced427dae · outbound

This paper cites A style-based generator architecture for generative adversarial networks.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation A style-based generator architecture for generative adversarial networks

Reference 20

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This paper cites Analyzing and improv- ing the image quality of stylegan.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Analyzing and improv- ing the image quality of stylegan

Reference 21

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Observation befcd4f9-07ef-44d1-8eff-5413d8aaf0d3 · outbound

This paper cites GGHead: Fast and Generalizable 3D Gaussian Heads.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation GGHead: Fast and Generalizable 3D Gaussian Heads

Reference 22

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Observation 44bd85d5-84b6-4b1d-8c60-4ca0ea6135b8 · outbound

This paper cites Gaus- sian3diff: 3d gaussian diffusion for 3d full head synthesis and editing.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Gaus- sian3diff: 3d gaussian diffusion for 3d full head synthesis and editing

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Observation 19d0e1e1-2a9a-4b14-a6cd-102b4858f22d · outbound

This paper cites Autoregressive image generation using resid- ual quantization.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Autoregressive image generation using resid- ual quantization

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This paper cites Controllable text-to-image generation.Advances in Neural Information Processing Systems, 32, 2019.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Controllable text-to-image generation.Advances in Neural Information Processing Systems, 32, 2019

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This paper cites an unresolved cited work.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Unresolved cited work

Reference 26

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This paper cites Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning.Advances in Neural Information Processing Systems, 35:17612–17625, 2022.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning.Advances in Neural Information Processing Systems, 35:17612–17625, 2022

Reference 27

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This paper cites Visual instruction tuning.Advances in neural information processing systems, 36, 2024.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Visual instruction tuning.Advances in neural information processing systems, 36, 2024

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This paper cites Which training methods for gans do actually converge? In International conference on machine learning, pages 3481–.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Which training methods for gans do actually converge? In International conference on machine learning, pages 3481–

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This paper cites Text2mesh: Text-driven neural stylization for meshes.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Text2mesh: Text-driven neural stylization for meshes

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Observation cdecfc0c-bb8f-40f3-b823-f10f2e6dd514 · outbound

This paper cites Text2facegan: Face generation from fine grained textual de- scriptions.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Text2facegan: Face generation from fine grained textual de- scriptions

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

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

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This paper cites Representation Learning with Contrastive Predictive Coding.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Representation Learning with Contrastive Predictive Coding

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This paper cites Paysan, R.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Paysan, R

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This paper cites Towards open-ended text-to-face generation, combination and manipulation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Towards open-ended text-to-face generation, combination and manipulation

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Observation a3fc27ad-1fec-4a4a-9a2d-25e40644444d · outbound

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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Learning transferable visual models from natural language supervi- sion

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This paper cites Zero-shot text-to-image generation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Zero-shot text-to-image generation

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Hierarchical Text-Conditional Image Generation with CLIP Latents

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This paper cites Generative adver- sarial text to image synthesis.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Generative adver- sarial text to image synthesis

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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Higher order contractive auto-encoder

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Observation c335105b-d031-4d89-a0ec-55b35709bc41 · outbound

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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation High-resolution image syn- thesis with latent diffusion models

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This paper cites Pho- torealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Pho- torealistic text-to-image diffusion models with deep language understanding.Advances in Neural Information Processing Systems, 35:36479–36494, 2022

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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Conditional Image Generation and Manipulation for User-Specified Content

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This paper cites Multi-caption text-to-face synthesis: Dataset and algo- rithm.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Multi-caption text-to-face synthesis: Dataset and algo- rithm

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Observation e8b7e879-7c43-44d6-9366-f1341cbc5b15 · outbound

This paper cites DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis

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Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Attention is all you need.Advances in neural information processing systems, 30, 2017

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Observation dbd661aa-31c7-4aa9-b57d-72a9b2714125 · outbound

This paper cites Faces a la carte: Text-to-face generation via attribute disentanglement.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Faces a la carte: Text-to-face generation via attribute disentanglement

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Observation c9486153-9104-45a6-a460-1bafc4d19fc0 · outbound

This paper cites High-fidelity 3d face genera- tion from natural language descriptions.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation High-fidelity 3d face genera- tion from natural language descriptions

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Observation 3950c2f3-7091-40ea-bc7f-7c152449f2a1 · outbound

This paper cites Tedigan: Text-guided diverse face image generation and ma- nipulation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Tedigan: Text-guided diverse face image generation and ma- nipulation

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This paper cites Omniavatar: Geometry-guided controllable 3d head syn- thesis.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Omniavatar: Geometry-guided controllable 3d head syn- thesis

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.453055Z

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Observation 0c14bdda-ff54-4a71-a0c6-dac0b2c93233 · outbound

This paper cites Attngan: Fine- grained text to image generation with attentional generative adversarial networks.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Attngan: Fine- grained text to image generation with attentional generative adversarial networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.441227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d43cd068-0b1e-4ce4-ad49-1798121f3570 · outbound

This paper cites Towards high-fidelity text-guided 3d face genera- tion and manipulation using only images.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Towards high-fidelity text-guided 3d face genera- tion and manipulation using only images

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.427768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e979a7b2-90d9-4ec4-8946-4ae5a8d52129 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T00:31:08.113817Z

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Unavailable: canonical work link unavailable.

Observation 8893a9f8-19ac-4385-9cb5-284c1d8f5228 · outbound

This paper cites Stack- gan: Text to photo-realistic image synthesis with stacked generative adversarial networks.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Stack- gan: Text to photo-realistic image synthesis with stacked generative adversarial networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.415061Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 30d6e647-65ee-4e1d-88ea-2f4c1f240ebd · outbound

This paper cites Stack- gan++: Realistic image synthesis with stacked generative adversarial networks.IEEE transactions on pattern analysis and machine intelligence, 41(8):1947–1962, 2018.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Stack- gan++: Realistic image synthesis with stacked generative adversarial networks.IEEE transactions on pattern analysis and machine intelligence, 41(8):1947–1962, 2018

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.403186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7c4839b6-b5d2-44ce-b479-1494bdf7799b · outbound

This paper cites DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T00:31:08.125192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 426d1768-4ea8-4558-9641-85788ae17f1b · outbound

This paper cites M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:31:08.203740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 334e4b03-11e9-4b82-ba61-618f27db6aa0 · outbound

This paper cites DiffGS: Functional Gaussian Splatting Diffusion.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation DiffGS: Functional Gaussian Splatting Diffusion

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:31:08.184971Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2cc47fac-b3db-41f9-b79e-1a1fd9f57f64 · outbound

This paper cites Generative adversarial network for text-to-face synthesis and manipulation.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Generative adversarial network for text-to-face synthesis and manipulation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.390930Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0881f328-e6c4-419e-bf31-7a9eaa0d5516 · outbound

This paper cites Generative adversar- ial network for text-to-face synthesis and manipulation with pretrained bert model.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation Generative adversar- ial network for text-to-face synthesis and manipulation with pretrained bert model

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.379003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3f82aeca-0e7a-4eb1-86bc-93ebdcf242c7 · outbound

This paper cites blonde”, “blue eyes.

Disentangling 3D from Large Vision-Language Models for Controlled Portrait Generation blonde”, “blue eyes

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:31:08.367112Z

Source-reported events for the cited work

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