Pith. sign in

REVIEW 3 major objections 3 minor 2 cited by

FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The abstract claims a visuotactile imitation learning framework, FBI, outperforms static-fusion baselines in simulation and the real world; the supplied full text, however, is an unrelated paper on text-to-image generation.

desk verdict The FBI abstract and metadata describe a robotics paper, but the full text is an unrelated text-to-image paper (MUSE); the central claim has no supporting content in the manuscript. read the letter →

arxiv 2508.14441 v1 pith:XNWXZAQF submitted 2025-08-20 cs.RO

Yijin Chen , Wenqiang Xu , Zhenjun Yu , Tutian Tang , Yutong Li , Siqiong Yao , Cewu Lu This is my paper
classification cs.RO
keywords in-handmanipulationtactilesensingvisuotactileimitationlearningdiffusionpolicydexterousdynamicsensorfusionlatentdynamicsmodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The abstract announces FBI, a framework for teaching a robotic hand to manipulate objects in hand by fusing touch and vision through a learned model of motion. It claims that this dynamic fusion beats methods that merge the two senses statically, across two custom and three standard dexterous tasks, in both simulation and the real world. A sympathetic reader would take the core claim as: tactile signals can be causally linked to object motion, and a policy that exploits that link is fast and general enough for real-time control. But the manuscript body supplied with this abstract is a different paper—MUSE, a text-to-image generation method by other authors—so none of the FBI experiments, baselines, or derivations are actually present for inspection. The claim, as submitted, is thus an abstract without its supporting text.

What carries the argument

The central objects named in the abstract are (1) a dynamics-aware latent model that is supposed to establish a causal link between tactile signals and object motion; (2) a transformer-based interaction module that fuses flow-derived tactile features with visual inputs; and (3) a one-step diffusion policy trained on the fused representation for real-time execution. The first is the load-bearing idea: it is what distinguishes 'dynamic' fusion from static fusion and what the rest of the pipeline is designed to feed. None of these components is described or derived anywhere in the attached full text.

What would settle it

Open the attached PDF and read it: it is a text-to-image paper by different authors with no robot experiments, which settles that the FBI claims are not backed by this manuscript. If the abstract is instead taken at face value, run one of the five named dexterous tasks (e.g., in-hand reorientation) with and without the dynamic tactile fusion and compare success rates.

Watch

Extended reading notes

Core claim

On the abstract's terms, the discovery is that in-hand manipulation can be treated as a visuotactile motion-prediction problem: instead of asking a policy to merge a vision snapshot with a tactile snapshot, FBI computes flow-derived tactile features and feeds them, together with visual features, into a dynamics-aware latent model that predicts how the object will move. A transformer interaction module fuses these, and a one-step diffusion policy turns the fused state into actions in real time. The stated result is that this dynamic-fusion pipeline outperforms static-fusion baselines on two customized and three standard dexterous manipulation tasks, in simulation and on real hardware. No text

Load-bearing premise

The load-bearing premise is document integrity—that the full text attached is the FBI paper; in fact it is an unrelated text-to-image manuscript, so the abstract's experimental claims are presently unsupported.

Editorial extensions

If this is right

  • If FBI works as claimed, in-hand policies could react to slip and contact changes in real time rather than relying on vision alone.
  • One-step diffusion policies would cut control latency below that of multi-step denoising baselines, making tactile-reactive manipulation practical on real hardware.
  • The causal-link claim implies tactile signals are predictive of object motion, so the learned dynamics model should transfer to new objects and grasps without retraining.
  • Outperforming static-fusion baselines on five tasks would show that temporal, flow-derived tactile features carry information that instantaneous contact readings miss.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the dynamics-aware latent model genuinely predicts object motion from touch, the same latent could double as a world model for contact-rich planning beyond imitation learning—an extension the abstract does not state.
  • Flow-derived tactile features resemble optical flow in vision; a shared motion representation across the two senses is a testable design principle that could simplify multimodal architectures.
  • A concrete test of the causal claim: record a tactile sequence from a sensorized hand and ask whether the latent model can forecast object pose for a short horizon using touch alone, without vision.
  • The document mismatch itself suggests that a check for metadata-to-body consistency—comparing author lists, abstracts, and section titles—could be automated during submission.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The submission metadata and abstract describe 'FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy', a robotics paper claiming a visuotactile imitation learning framework that dynamically fuses touch and vision for real-time in-hand manipulation, with experiments in simulation and the real world on five tasks. However, the full text is a completely different paper, 'MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion' (arXiv:2508.14440v1), a text-to-image generation paper about layout-controllable multi-subject synthesis. The body contains no description of FBI, no dynamics-aware latent model, no transformer interaction module, no one-step diffusion policy, no simulation, no real-robot experiments, no baselines, and no ablation studies for the claimed contribution. The only FBI-related content is the abstract itself, which asserts the central claims without any supporting evidence in the manuscript. As submitted, the article does not contain the research it claims to present.

Significance. If the abstract's claims were supported, FBI would be a potentially significant contribution to visuotactile imitation learning for dexterous manipulation: dynamic fusion of tactile and visual streams via a dynamics-aware latent model, combined with a real-time one-step diffusion policy, would be a useful advance over static-fusion baselines. However, none of those ingredients can be evaluated from the submitted document. The manuscript contains no machine-checked proofs, no reproducible code, no derivations, and no experimental data for FBI. The scientific record as submitted therefore provides no basis for assessing the claimed contribution. The mismatch between abstract and body is not a presentation issue; it invalidates the core claim.

major comments (3)
  1. [Abstract vs. full text (all sections)] The central claim—'the proposed method outperforms the baseline methods in both simulation and the real world'—appears only in the abstract. The full text from Section 1 through Section 5 is the MUSE paper on text-to-image generation. Section 3 describes concatenated cross-attention (CCA) and decoupled cross-attention (DCA) for layout control and subject synthesis; Section 4 reports layout success rates on MIG Bench and MS-Bench. There is no in-hand manipulation task, no tactile sensor, no robot, and no baseline method for FBI anywhere in the manuscript. This is a load-bearing integrity failure: the reviewed artifact does not contain the research described by its own abstract.
  2. [§3 (Method), Eq. (1)–(6)] The abstract introduces a 'dynamics-aware latent model', a 'transformer-based interaction module' for flow-derived tactile features, and a 'one-step diffusion policy'. None of these components appears in the body. The method section defines only cross-attention variants for image generation: Eq. (1) is standard cross-attention, Eq. (2) is decoupled cross-attention, Eq. (4) is concatenated cross-attention, and Eq. (5) is their combination. There is no equation or derivation relating tactile signals to object motion, and the claimed 'causal link' is asserted without any formal or experimental support.
  3. [§4 (Experiments), Tables 1–4] The experiments section evaluates MUSE on image generation benchmarks, not on robot manipulation. Table 1 reports layout success rates on MIG Bench; Table 2 reports CLIP-based metrics on MS-Bench and MS-Bench-Random; Tables 3 and 4 report ablations of attention mechanisms and training strategies. There are no simulation environments, no real-world robot trials, no error bars, and no comparison to the baselines named in the abstract. Consequently, the abstract's claim of empirical superiority cannot be checked. The absence of experimental data is not a minor omission; it removes the entire evidentiary basis for the paper's central assertion.
minor comments (3)
  1. [Title and authorship] The metadata title and author list refer to FBI, while the body title and authors refer to MUSE. This makes it impossible for a reader to identify the responsible authors of the claimed work without external information.
  2. [References] The reference list is entirely for the MUSE paper, covering text-to-image generation, layout control, and subject synthesis. No references to tactile sensing, imitation learning, dexterous manipulation, or diffusion policies for robotics appear in the body, leaving the abstract's claims disconnected from any prior work in the field.
  3. [Figures] Figures 1–9 illustrate layout-controllable image generation and are unrelated to visuotactile manipulation. There is no architecture diagram for the claimed FBI pipeline, and no qualitative result showing in-hand object manipulation.

Circularity Check

2 steps flagged · score 8.0 of 10

FBI's central claim is asserted only in the abstract; the manuscript body is an unrelated text-to-image paper (MUSE), so the claimed experimental demonstration is the claim itself.

  1. self definitional [Abstract, paragraph 2, sentence 5 (compared with entire full text)]
    "Extensive experiments demonstrate that the proposed method outperforms the baseline methods in both simulation and the real world on two customized in-hand manipulation tasks and three standard dexterous manipulation tasks."

    The only place this sentence's referent can be found is the abstract itself. The full text is MUSE: Multi-Subject Unified Synthesis via Explicit Layout Semantic Expansion (Fei Peng et al., arXiv:2508.14440v1), a text-to-image paper with no FBI method, no dynamics-aware latent model, no transformer interaction module, no one-step diffusion policy, no robotic platform, and no visuotactile experiments or baselines. Thus 'extensive experiments demonstrate X' is the sole evidence for X, and that evidence is the same assertion. The claimed derivation chain is not merely incomplete; the predicted performance and its support are the same abstract text by construction.

  2. other [Abstract, paragraph 1, sentence 4 (causal-link premise)]
    "Unlike prior static fusion methods, FBI establishes a causal link between tactile signals and object motion via a dynamics-aware latent model."

    This load-bearing premise is never defined or derived in the manuscript. The body (MUSE) contains no dynamics-aware latent model, no tactile/visuotactile formalism, and no derivation of a causal link; the MUSE method uses concatenated cross-attention for image layout, not object-motion dynamics. Therefore the framework's claimed innovation rests on an undefined assertion, and no independent derivation from first principles exists to audit. This is a missing-proof/omitted-derivation step that leaves the central claim reducible to the abstract's wording.

full rationale

The submission labeled arXiv:2508.14441 (cs.RO) consists of an FBI abstract and metadata attached to an unrelated manuscript, MUSE (arXiv:2508.14440v1), by different authors. Because the body text contains none of the FBI components and none of the promised experiments, there is no equation chain, no fitted parameter, and no benchmark result that could be checked for equivalence to the inputs. The single concrete 'reduction' that can be exhibited is that the claim 'extensive experiments demonstrate superiority' appears exactly once, in the abstract, and the manuscript provides no other content to which it could refer; the claim and its evidence are therefore the same text. This is a self-assertion rather than a formal self-citation loop, but it is the strongest identifiable circularity: the central empirical conclusion is not derived from any independent measurement, external benchmark, or reproducibility artifact included in the manuscript. No self-citations by the FBI authors appear, so patterns 3-5 are not present; the MUSE portion has its own experiments, but those experiments are irrelevant to FBI. The document-integrity failure is severe: the paper is unverifiable as a robotics paper. Score 8 reflects that the FBI conclusion is forced by the abstract's assertion, with no independent content in the body, rather than a 10, because the MUSE body is at least a coherent, self-contained (if unrelated) text.

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The central FBI claim has no quantitative content in this manuscript: no parameters, equations, data, or experiments. The free parameters and the CCA entity listed here are drawn from the body text, which is the unrelated MUSE paper; they are listed to be exhaustive about what the manuscript actually contains. None of them support the FBI claim, and the first axiom records the integrity premise that fails.

free parameters (3)
  • DCA control scale λ (body text, MUSE paper) = 0.8
    Set 'empirically' in body Sec. 4.1 and ablated in its Table 5. Belongs to the unrelated MUSE method, not to FBI.
  • Layout grounding hyperparameters (body text, MUSE paper) = Fourier freq 16; 4 resampler queries
    Body Supp. A sets Fourier frequency 16 and 4 learnable query tokens for image grounding tokens; specific to the unrelated MUSE method.
  • CFG guidance scale (body text, MUSE paper) = 7.5
    Body Sec. 4.1 sets classifier-free guidance to 7.5 for inference; standard but hand-set.
assumptions (4)
  • ad hoc to paper The full text is the manuscript described by the metadata (arXiv 2508.14441, FBI).
    Load-bearing and false: the body is arXiv 2508.14440v1, MUSE, a different paper by different authors. Every FBI claim inherits this integrity premise.
  • domain assumption A causal link between tactile signals and object motion can be learned inside a dynamics-aware latent model.
    Asserted in the abstract as the basis of the fusion; no derivation, data, or citations support it in this manuscript.
  • domain assumption Fusing touch and vision through flow-derived features outperforms single-modality or static-fusion policies.
    The abstract's motivating contrast against 'prior static fusion methods'; the body contains no comparison for FBI.
  • domain assumption Pre-trained SDXL and CLIP models provide usable priors for controllable generation (body text, MUSE).
    The unrelated MUSE body builds on SDXL, CLIP, and Grounding DINO as given tools; listed for completeness of the body's own assumptions.
invented entities (2)
  • Flow Before Imitation (FBI) framework and dynamics-aware latent model
    purpose: Claimed method for fusing visuotactile signals to control dexterous robot hands
    Defined only in the abstract; the body contains no implementation, equations, code, or experiments, so there is no falsifiable handle outside the abstract's own words.
  • Concatenated cross-attention (CCA) (body text, MUSE) independent evidence
    purpose: Body-text mechanism to merge layout and text conditions in image generation without control collision
    Specified by an equation (body Eq. 4) and measured on benchmarks (body Tables 1, 3), so it has independent evidence within the body; but it belongs to the unrelated MUSE paper and does not support FBI.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy." pith.science (2026). https://pith.science/paper/XNWXZAQF

@misc{pith2026250814441,
  author       = {Pith},
  title        = {Pith review of: FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XNWXZAQF}},
  note         = {Machine review of arXiv:2508.14441}
}
read the original abstract

Dexterous in-hand manipulation is a long-standing challenge in robotics due to complex contact dynamics and partial observability. While humans synergize vision and touch for such tasks, robotic approaches often prioritize one modality, therefore limiting adaptability. This paper introduces Flow Before Imitation (FBI), a visuotactile imitation learning framework that dynamically fuses tactile interactions with visual observations through motion dynamics. Unlike prior static fusion methods, FBI establishes a causal link between tactile signals and object motion via a dynamics-aware latent model. FBI employs a transformer-based interaction module to fuse flow-derived tactile features with visual inputs, training a one-step diffusion policy for real-time execution. Extensive experiments demonstrate that the proposed method outperforms the baseline methods in both simulation and the real world on two customized in-hand manipulation tasks and three standard dexterous manipulation tasks. Code, models, and more results are available in the website https://sites.google.com/view/dex-fbi.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph

    cs.CV 2026-08 conditional novelty 5.0 of 10

    PhotoHOI turns one RGB photo plus an open-vocabulary instruction into a scene-grounded 3D hand-object motion sequence by parsing the task, recovering objects, planning object motion, and optimizing grasps in a learned...

  2. TacRefineNet: Goal-Conditioned Tactile Grasp Refinement for Edge-Prominent Objects

    cs.RO 2025-09 conditional novelty 5.0 of 10

    A robot hand uses fingertip pressure images to iteratively re-grasp thin objects, aligning them to a demonstrated target pose within a few millimeters using no vision.

Reference graph

Works this paper leans on

65 extracted references · 36 canonical work pages · cited by 2 Pith papers

  1. [1]

    Spatext: Spatio-textual representation for con- trollable image generation

    Omri Avrahami, Thomas Hayes, Oran Gafni, Sonal Gupta, Yaniv Taigman, Devi Parikh, Dani Lischinski, Ohad Fried, and Xi Yin. Spatext: Spatio-textual representation for con- trollable image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18370–18380, 2023. 2

    work page 2023

  2. [2]

    ediff-i: Text-to-image dif- fusion models with an ensemble of expert denoisers

    Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Ji- aming Song, Qinsheng Zhang, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, et al. ediff-i: Text-to-image dif- fusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022. 2

    arXiv 2022

  3. [3]

    Multidiffusion: Fusing diffusion paths for controlled image generation

    Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. 2023. 2

    work page 2023

  4. [4]

    Improving image generation with better captions

    James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf, 2(3):8, 2023. 1, 2

    work page 2023

  5. [5]

    Large scale gan training for high fidelity natural image synthesis

    Andrew Brock. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 , 2018

    arXiv 2018

  6. [6]

    Muse: Text-to-image generation via masked generative transform- ers

    Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, Jose Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Mur- phy, William T Freeman, Michael Rubinstein, et al. Muse: Text-to-image generation via masked generative transform- ers. arXiv preprint arXiv:2301.00704, 2023. 1, 2

    arXiv 2023

  7. [7]

    Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models

    Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG), 42(4):1–10, 2023. 2

    work page 2023

  8. [8]

    Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis

    Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al. Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023. 1, 2

    arXiv 2023

Show all 65 references
  1. [9]

    Training-free layout control with cross-attention guidance

    Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guidance. In Proceed- ings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5343–5353, 2024. 2

    2024

  2. [10]

    Anydoor: Zero-shot object-level im- age customization

    Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level im- age customization. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 6593–6602, 2024. 2

    2024

  3. [11]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural informa- tion processing systems, 34:8780–8794, 2021. 1, 2

    2021

  4. [12]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. In Forty-first International Conference on Mach...

    2024

  5. [13]

    Training-free structured diffusion guidance for compositional text-to-image synthesis

    Weixi Feng, Xuehai He, Tsu-Jui Fu, Varun Jampani, Arjun Akula, Pradyumna Narayana, Sugato Basu, Xin Eric Wang, and William Yang Wang. Training-free structured diffusion guidance for compositional text-to-image synthesis. arXiv preprint arXiv:2212.05032, 2022. 2

    2022 arXiv

  6. [14]

    Make-a-scene: Scene- based text-to-image generation with human priors

    Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman. Make-a-scene: Scene- based text-to-image generation with human priors. In Eu- ropean Conference on Computer Vision , pages 89–106. Springer, 2022. 2

    2022

  7. [15]

    An image is worth one word: Personalizing text-to- image generation using textual inversion

    Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patash- nik, Amit H Bermano, Gal Chechik, and Daniel Cohen- Or. An image is worth one word: Personalizing text-to- image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2

    2022 arXiv

  8. [16]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Commu- nications of the ACM, 63(11):139–144, 2020. 2

    2020

  9. [17]

    Vec- tor quantized diffusion model for text-to-image synthesis

    Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo. Vec- tor quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 10696–10706, 2022. 1, 2

    2022

  10. [18]

    Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models

    Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yun- peng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, et al. Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models. Advances in Neural Information Processing ...

    2023

  11. [19]

    Svdiff: Compact param- eter space for diffusion fine-tuning

    Ligong Han, Yinxiao Li, Han Zhang, Peyman Milanfar, Dimitris Metaxas, and Feng Yang. Svdiff: Compact param- eter space for diffusion fine-tuning. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 7323–7334, 2023. 2

    2023

  12. [20]

    Clipscore: A reference-free evaluation met- ric for image captioning

    Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation met- ric for image captioning. arXiv preprint arXiv:2104.08718,

    arXiv

  13. [21]

    Classifier-free diffusion guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 6

    2022 arXiv

  14. [22]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 1, 2

    2020

  15. [23]

    Realcustom: narrowing real text word for real-time open-domain text-to-image customization

    Mengqi Huang, Zhendong Mao, Mingcong Liu, Qian He, and Yongdong Zhang. Realcustom: narrowing real text word for real-time open-domain text-to-image customization. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition , pages 7476–7485, 2024. 2, 1

    2024

  16. [24]

    Dense text-to-image generation with attention modulation

    Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung-Woo Ha, and Jun-Yan Zhu. Dense text-to-image generation with attention modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7701–7711, 2023. 2

    2023

  17. [25]

    Auto-encoding variational bayes

    Diederik P Kingma. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 2 9

    2013 arXiv

  18. [26]

    Gen- erating images with multimodal language models

    Jing Yu Koh, Daniel Fried, and Russ R Salakhutdinov. Gen- erating images with multimodal language models. Advances in Neural Information Processing Systems, 36, 2024. 2

    2024

  19. [27]

    Multi-concept customization of text-to-image diffusion

    Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 1931–1941, 2023. 2

    1931

  20. [28]

    Blip-diffusion: Pre- trained subject representation for controllable text-to-image generation and editing

    Dongxu Li, Junnan Li, and Steven Hoi. Blip-diffusion: Pre- trained subject representation for controllable text-to-image generation and editing. Advances in Neural Information Pro- cessing Systems, 36:30146–30166, 2023. 2

    2023

  21. [29]

    Gligen: Open-set grounded text-to-image generation

    Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jian- wei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22511–22521, 2023. 2,...

    2023

  22. [30]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceeding...

    2014

  23. [31]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Qing Jiang, Chunyuan Li, Jianwei Yang, Hang Su, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499, 2023. 6

    2023 arXiv

  24. [32]

    Subject- diffusion: Open domain personalized text-to-image genera- tion without test-time fine-tuning

    Jian Ma, Junhao Liang, Chen Chen, and Haonan Lu. Subject- diffusion: Open domain personalized text-to-image genera- tion without test-time fine-tuning. In ACM SIGGRAPH 2024 Conference Papers, pages 1–12, 2024. 2, 3

    2024

  25. [33]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021

    Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021. 2

    2021 arXiv

  26. [34]

    Kosmos-g: Generating images in context with multimodal large language models

    Xichen Pan, Li Dong, Shaohan Huang, Zhiliang Peng, Wenhu Chen, and Furu Wei. Kosmos-g: Generating images in context with multimodal large language models. arXiv preprint arXiv:2310.02992, 2023. 3

    2023 arXiv

  27. [35]

    λ-eclipse: Multi-concept personalized text-to-image diffusion models by leveraging clip latent space

    Maitreya Patel, Sangmin Jung, Chitta Baral, and Yezhou Yang. λ-eclipse: Multi-concept personalized text-to-image diffusion models by leveraging clip latent space. arXiv preprint arXiv:2402.05195, 2024. 2, 3

    2024 arXiv

  28. [36]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 1, 2, 3, 6

    2023 arXiv

  29. [37]

    Controlling text-to-image diffusion by orthogo- nal finetuning

    Zeju Qiu, Weiyang Liu, Haiwen Feng, Yuxuan Xue, Yao Feng, Zhen Liu, Dan Zhang, Adrian Weller, and Bernhard Sch¨olkopf. Controlling text-to-image diffusion by orthogo- nal finetuning. Advances in Neural Information Processing Systems, 36:79320–79362, 2023. 2

    2023

  30. [38]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  31. [39]

    Hierarchical text-conditional image gener- ation with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 1, 2

    2022 arXiv

  32. [40]

    Kandinsky: an improved text-to-image syn- thesis with image prior and latent diffusion

    Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Malt- seva, Vladimir Arkhipkin, Igor Pavlov, Ilya Ryabov, An- gelina Kuts, Alexander Panchenko, Andrey Kuznetsov, and Denis Dimitrov. Kandinsky: an improved text-to-image syn- thesis with image prior and latent diffusion. arXiv p...

    2023 arXiv

  33. [41]

    Generative ad- versarial text to image synthesis

    Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Lo- geswaran, Bernt Schiele, and Honglak Lee. Generative ad- versarial text to image synthesis. InInternational conference on machine learning, pages 1060–1069. PMLR, 2016. 2

    2016

  34. [42]

    Move anything with layered scene diffusion

    Jiawei Ren, Mengmeng Xu, Jui-Chieh Wu, Ziwei Liu, Tao Xiang, and Antoine Toisoul. Move anything with layered scene diffusion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6380– 6389, 2024. 2, 4

    2024

  35. [43]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1, 2, 3

    2022

  36. [44]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2250...

    2023

  37. [45]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information...

    2022

  38. [46]

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

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural In- f...

    2022

  39. [47]

    Noisecollage: A layout-aware text-to-image diffusion model based on noise cropping and merging

    Takahiro Shirakawa and Seiichi Uchida. Noisecollage: A layout-aware text-to-image diffusion model based on noise cropping and merging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8921–8930, 2024. 2, 4

    2024

  40. [48]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 2, 6

    2010 arXiv

  41. [49]

    Score-based generative modeling through stochastic differential equa- tions

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equa- tions. arXiv preprint arXiv:2011.13456, 2020. 1, 2 10

    2011 arXiv

  42. [50]

    Generative multimodal mod- els are in-context learners

    Quan Sun, Yufeng Cui, Xiaosong Zhang, Fan Zhang, Qiy- ing Yu, Yueze Wang, Yongming Rao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Generative multimodal mod- els are in-context learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , ...

    2024

  43. [51]

    Diffusers: State-of-the-art diffu- sion models

    Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf. Diffusers: State-of-the-art diffu- sion models. https://github.com/huggingface/ diffusers...

    2022

  44. [52]

    Instantid: Zero-shot identity-preserving generation in seconds

    Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, Anthony Chen, Huaxia Li, Xu Tang, and Yao Hu. Instantid: Zero-shot identity-preserving generation in seconds. arXiv preprint arXiv:2401.07519, 2024. 2, 3, 4, 1

    2024 arXiv

  45. [53]

    Instancediffusion: Instance- level control for image generation

    Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Ro- hit Girdhar, and Ishan Misra. Instancediffusion: Instance- level control for image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6232–6242, 2024. 2, 3, 4, 7

    2024

  46. [54]

    Ms-diffusion: Multi-subject zero-shot im- age personalization with layout guidance

    X Wang, Siming Fu, Qihan Huang, Wanggui He, and Hao Jiang. Ms-diffusion: Multi-subject zero-shot im- age personalization with layout guidance. arXiv preprint arXiv:2406.07209, 2024. 2, 3, 4, 5, 6, 7, 1

    2024 arXiv

  47. [55]

    Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation

    Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo. Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15943–15953, 2023. 2

    2023

  48. [56]

    Ifadapter: Instance feature con- trol for grounded text-to-image generation

    Yinwei Wu, Xianpan Zhou, Bing Ma, Xuefeng Su, Kai Ma, and Xinchao Wang. Ifadapter: Instance feature con- trol for grounded text-to-image generation. arXiv preprint arXiv:2409.08240, 2024. 2, 3, 4

    2024 arXiv

  49. [57]

    Multigen: Zero-shot image generation from multi- modal prompts

    Zhi-Fan Wu, Lianghua Huang, Wei Wang, Yanheng Wei, and Yu Liu. Multigen: Zero-shot image generation from multi- modal prompts. In European Conference on Computer Vi- sion, pages 297–313. Springer, 2024. 3

    2024

  50. [58]

    Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion

    Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wen- tian Zhang, Yefeng Zheng, and Mike Zheng Shou. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7452–7461, 2023. 2

    2023

  51. [59]

    Reco: Region-controlled text-to-image genera- tion

    Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region-controlled text-to-image genera- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 14246–14255,

  52. [60]

    Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip- adapter: Text compatible image prompt adapter for text-to- image diffusion models. arXiv preprint arXiv:2308.06721,

    arXiv

  53. [61]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 2

    2023

  54. [62]

    Recognize anything: A strong image tagging model

    Youcai Zhang, Xinyu Huang, Jinyu Ma, Zhaoyang Li, Zhaochuan Luo, Yanchun Xie, Yuzhuo Qin, Tong Luo, Yaqian Li, Shilong Liu, et al. Recognize anything: A strong image tagging model. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 17...

    2024

  55. [63]

    Ssr-encoder: Encoding selective subject representation for subject-driven generation

    Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, et al. Ssr-encoder: Encoding selective subject representation for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Rec...

    2024

  56. [64]

    Migc++: Advanced multi-instance generation controller for image synthesis

    Dewei Zhou, You Li, Fan Ma, Zongxin Yang, and Yi Yang. Migc++: Advanced multi-instance generation controller for image synthesis. arXiv preprint arXiv:2407.02329, 2024. 2, 5

    2024 arXiv

  57. [65]

    Migc: Multi-instance generation controller for text-to-image synthesis

    Dewei Zhou, You Li, Fan Ma, Xiaoting Zhang, and Yi Yang. Migc: Multi-instance generation controller for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6818– 6828, 2024. 2, 3, 4, 6, 7 11 MUSE: Multi-Subject ...

    2024

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.