Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands

Hak Gu Kim; Minho Park; Yong Man Ro

arxiv: 1907.01187 · v1 · pith:3GHG345Knew · submitted 2019-07-02 · 💻 cs.CV

Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands

Minho Park , Hak Gu Kim , Yong Man Ro This is my paper

Pith reviewed 2026-05-25 11:25 UTC · model grok-4.3

classification 💻 cs.CV

keywords generative modelsimage synthesisdiscriminatorsrealistic image generationlarge variationsappearance preservationvariation transformationGAN

0 comments

The pith

Generative guiding blocks with two discriminators enhance latent features in generative models to produce realistic images with large variations.

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

The paper proposes generative guiding blocks to address the difficulty of generating realistic images that also show large changes such as major pose shifts or spatial deformations. Each block contains a realistic appearance preserving discriminator and a naturalistic variation transforming discriminator. These components are inserted into a generative model so that the latent features at specific layers are guided to satisfy both realism and the target variation at the same time. A reader would care because ordinary generative models often trade off one requirement against the other when the demanded change is large. The method is presented as a modular addition that improves synthesis quality on both qualitative and quantitative measures.

Core claim

The central claim is that taking the proposed generative guiding blocks into a generative model enhances the latent features at the layer of the generative model to synthesize both realistic looking and target variation images, where the blocks consist of a realistic appearance preserving discriminator and a naturalistic variation transforming discriminator.

What carries the argument

Generative guiding blocks, modules inserted into the generator that contain a realistic appearance preserving discriminator and a naturalistic variation transforming discriminator to steer latent features.

If this is right

The blocks enable generation of images with large spatial deformations while preserving perceptual realism.
The approach improves both appearance fidelity and variation accuracy simultaneously compared to prior methods.
The blocks can be added to existing generative architectures to handle cases with large pose or deformation demands.
Joint training of the generator with the two discriminators yields enhanced latent representations at chosen layers.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The blocks might allow training on unpaired data if the discriminators learn variation directions without explicit pairs.
The same mechanism could extend to video frame synthesis where consecutive frames require large motion changes.
Testing integration with non-adversarial generators such as autoencoders would show whether the guidance is specific to GAN training.

Load-bearing premise

The two new discriminators can be trained jointly with the generator without destabilizing the overall adversarial process or needing extensive domain-specific hyperparameter tuning.

What would settle it

Training a standard generative model with and without the guiding blocks on the same large-variation task and finding that the version with the blocks produces either less realistic images or smaller achieved variations than the baseline would falsify the claim.

read the original abstract

Realistic image synthesis is to generate an image that is perceptually indistinguishable from an actual image. Generating realistic looking images with large variations (e.g., large spatial deformations and large pose change), however, is very challenging. Handing large variations as well as preserving appearance needs to be taken into account in the realistic looking image generation. In this paper, we propose a novel realistic looking image synthesis method, especially in large change demands. To do that, we devise generative guiding blocks. The proposed generative guiding block includes realistic appearance preserving discriminator and naturalistic variation transforming discriminator. By taking the proposed generative guiding blocks into generative model, the latent features at the layer of generative model are enhanced to synthesize both realistic looking- and target variation- image. With qualitative and quantitative evaluation in experiments, we demonstrated the effectiveness of the proposed generative guiding blocks, compared to the state-of-the-arts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper adds two discriminators in a guiding block to balance realism and large variations in GAN image synthesis, but supplies no loss details or training mechanics to support the claim.

read the letter

Colleague, the main thing here is that the authors propose a generative guiding block containing a realistic appearance preserving discriminator and a naturalistic variation transforming discriminator. They insert these into a generative model so that latent features at certain layers can produce images that are both realistic and capable of large changes like pose shifts. The abstract frames this as a practical fix for a known weakness in current synthesis methods. They claim qualitative and quantitative gains over prior work. That framing of the problem is reasonable and the split-objective idea is a logical response to the trade-off between fidelity and deformation. The paper is aimed at computer vision people doing conditional generation or pose-guided editing, and a reader in that area might see the named block as a small architectural tweak worth testing if the full version has code or more specs. The soft spots are the lack of any loss equations, weighting scheme, update schedule, or ablation results. Nothing addresses how the two discriminators are kept from producing conflicting gradients during joint training with the generator. The stress-test concern lands because the central claim of enhanced latent features depends on stable multi-discriminator optimization, and the text gives no evidence that this was achieved. Without those pieces the evaluations cannot be assessed. I would not bring this to a reading group, would not cite it, and would not send it for peer review until the method is specified enough for others to reproduce or critique the training process.

Referee Report

2 major / 1 minor

Summary. The paper claims that inserting generative guiding blocks—each containing a realistic appearance preserving discriminator and a naturalistic variation transforming discriminator—into a generative model enhances the latent features at a chosen layer, enabling synthesis of images that are simultaneously realistic-looking and capable of large target variations (e.g., spatial deformations or pose changes). Effectiveness is asserted on the basis of qualitative and quantitative experiments that compare the approach to state-of-the-art methods.

Significance. If the central architectural claim can be substantiated with the missing technical details, the dual-discriminator guiding-block construction could offer a practical way to mitigate the well-known tension between realism and large-variation synthesis in GANs. The work would then constitute a modest but concrete architectural contribution to conditional or unconditional image generation pipelines.

major comments (2)

[Abstract] Abstract: the claim that the generative guiding blocks 'enhance' latent features to produce both realistic and large-variation images rests entirely on an unevidenced assertion; the abstract supplies neither loss formulations, weighting coefficients, nor any description of the joint optimization schedule for the two added discriminators and the generator.
[Abstract] Abstract / implied Methods: no dataset descriptions, training hyperparameters, convergence diagnostics, or ablation results are provided. Without these, it is impossible to verify whether the asserted latent-feature enhancement actually occurs or whether the multi-discriminator objective simply destabilizes training, as is common when additional discriminators are introduced without explicit balancing.

minor comments (1)

[Abstract] Abstract contains minor phrasing and grammatical issues: 'Handing large variations' should read 'Handling large variations'; the hyphenated construction 'realistic looking- and target variation- image' is unclear and should be reworded for precision.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate where revisions will be made to strengthen the presentation.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the generative guiding blocks 'enhance' latent features to produce both realistic and large-variation images rests entirely on an unevidenced assertion; the abstract supplies neither loss formulations, weighting coefficients, nor any description of the joint optimization schedule for the two added discriminators and the generator.

Authors: The abstract provides a high-level summary of the contribution. Detailed loss formulations for the realistic appearance preserving discriminator and naturalistic variation transforming discriminator (including weighting coefficients and the joint optimization schedule with the generator) appear in Section 3, Equations (3)–(6), and the training procedure in Section 3.3. We agree the abstract could more explicitly reference these elements to support the enhancement claim and will revise it accordingly. revision: yes
Referee: [Abstract] Abstract / implied Methods: no dataset descriptions, training hyperparameters, convergence diagnostics, or ablation results are provided. Without these, it is impossible to verify whether the asserted latent-feature enhancement actually occurs or whether the multi-discriminator objective simply destabilizes training, as is common when additional discriminators are introduced without explicit balancing.

Authors: Dataset descriptions are given in Section 4.1, training hyperparameters and convergence diagnostics in Section 4.2, and ablation studies (including stability analysis) in Section 4.4. These sections provide evidence that the dual-discriminator objective does not destabilize training and that latent-feature enhancement occurs. To improve accessibility from the abstract, we will add a brief reference to the experimental validation sections in a revised abstract. revision: partial

Circularity Check

0 steps flagged

No circularity: architectural proposal with no derivations or fitted quantities

full rationale

The paper introduces generative guiding blocks as an architectural addition to GANs, consisting of two new discriminators. The abstract and description contain no equations, no loss functions, no parameter-fitting steps, and no derivations. Claims rest on qualitative/quantitative experiments rather than any mathematical reduction to inputs. No self-citation chains, self-definitional constructs, or renamed empirical patterns are present. The central assertion (latent-feature enhancement via the blocks) is presented as an empirical outcome of the architecture, not a quantity derived by construction from fitted values or prior self-work. This matches the default expectation of a non-circular empirical methods paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on the standard GAN training assumptions and the unstated premise that the new discriminators integrate stably; no free parameters, invented physical entities, or ad-hoc axioms are explicitly introduced in the abstract.

axioms (1)

domain assumption Adversarial training of generator and discriminator networks converges to a useful equilibrium when additional guidance signals are added at intermediate layers.
Implicit in the claim that the guiding blocks enhance latent features without further justification.

pith-pipeline@v0.9.0 · 5684 in / 1340 out tokens · 19834 ms · 2026-05-25T11:25:33.541445+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The proposed generative guiding block includes realistic appearance preserving discriminator and naturalistic variation transforming discriminator. By taking the proposed generative guiding blocks into generative model, the latent features at the layer of generative model are enhanced...
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

LGGB = Σ λn_RAPD ℓn_RAPD + λn_NVTD ℓn_NVTD + ℓn_rec

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages · 4 internal anchors

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Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands

INTRODUCTION Generating realistic-looking images draws great attention and considered as an important task in generative models for image synthesis. Recently, deep learning-based generative models have achieved remarkable success in various syn- thesis tasks such as face, human, and scene generation. In data acquisition, it is time consuming and costly to...

work page internal anchor Pith review Pith/arXiv arXiv 2017
[2]

1 shows the proposed generative model with generative guiding blocks (GGBs)

PROPOSED METHOD Fig. 1 shows the proposed generative model with generative guiding blocks (GGBs). The generator synthesizes the fake image having the appearance of the input image and the target variants. The discriminator determines whether the fake im- age is real or not. As shown in Fig. 1, the generative guiding blocks (GGBs) are attached to multi-lev...

work page
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Datasets For verifying the effectiveness of the proposed generative model with GGBs, we used public datasets: DeepFash- ion [22]

EXPERIMENTS AND RESULTS 3.1. Datasets For verifying the effectiveness of the proposed generative model with GGBs, we used public datasets: DeepFash- ion [22]. This dataset consists of 52,712 in-shop clothes images with 256×256 resolution. As similar to [16], for the training set, we have 146,680 pairs. Each pair is composed of two images of the same ident...

work page
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The pro- posed GGB consisted of two critic networks which were RAPD for maintaining the appearance characteristic and NVTD for applying the target variants

CONCLUSION In this paper, we proposed a novel Generative Guiding Block for synthesizing realistic looking images with the large vari- ations while preserving the appearance properties. The pro- posed GGB consisted of two critic networks which were RAPD for maintaining the appearance characteristic and NVTD for applying the target variants. By hierarchical...

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Photo-realistic fa- cial emotion synthesis using multi-level critic networks with multi-level generative model,

M. Park, H. G. Kim, and Y . M. Ro, “Photo-realistic fa- cial emotion synthesis using multi-level critic networks with multi-level generative model,” inMultiMedia Mod- eling, Cham, 2019, pp. 3–15, Springer International Publishing

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[1] [1]

Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands

INTRODUCTION Generating realistic-looking images draws great attention and considered as an important task in generative models for image synthesis. Recently, deep learning-based generative models have achieved remarkable success in various syn- thesis tasks such as face, human, and scene generation. In data acquisition, it is time consuming and costly to...

work page internal anchor Pith review Pith/arXiv arXiv 2017

[2] [2]

1 shows the proposed generative model with generative guiding blocks (GGBs)

PROPOSED METHOD Fig. 1 shows the proposed generative model with generative guiding blocks (GGBs). The generator synthesizes the fake image having the appearance of the input image and the target variants. The discriminator determines whether the fake im- age is real or not. As shown in Fig. 1, the generative guiding blocks (GGBs) are attached to multi-lev...

work page

[3] [3]

Datasets For verifying the effectiveness of the proposed generative model with GGBs, we used public datasets: DeepFash- ion [22]

EXPERIMENTS AND RESULTS 3.1. Datasets For verifying the effectiveness of the proposed generative model with GGBs, we used public datasets: DeepFash- ion [22]. This dataset consists of 52,712 in-shop clothes images with 256×256 resolution. As similar to [16], for the training set, we have 146,680 pairs. Each pair is composed of two images of the same ident...

work page

[4] [4]

The pro- posed GGB consisted of two critic networks which were RAPD for maintaining the appearance characteristic and NVTD for applying the target variants

CONCLUSION In this paper, we proposed a novel Generative Guiding Block for synthesizing realistic looking images with the large vari- ations while preserving the appearance properties. The pro- posed GGB consisted of two critic networks which were RAPD for maintaining the appearance characteristic and NVTD for applying the target variants. By hierarchical...

work page

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Auto-Encoding Variational Bayes

D. P. Kingma and M. Welling, “Auto-encoding varia- tional bayes.,” CoRR, vol. abs/1312.6114, 2013

work page internal anchor Pith review Pith/arXiv arXiv 2013

[6] [6]

Generative adversarial nets,

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y . Ben- gio, “Generative adversarial nets,” in Advances in Neu- ral Information Processing Systems 27 , pp. 2672–2680. Curran Associates, Inc., 2014

work page 2014

[7] [7]

Pixel recurrent neural networks,

A. Van Den Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” in ICML, 2016, pp. 1747–1756

work page 2016

[8] [8]

Semantic im- age inpainting with deep generative models,

R. A. Yeh ∗, C. Chen ∗, T. Y . Lim, A. G. Schwing, M. HasegawaJohnson, and M. N. Do, “Semantic im- age inpainting with deep generative models,” in CVPR, 2017, ∗ equal contribution

work page 2017

[9] [9]

Neural face editing with intrinsic image disentangling,

Z. Shu, E. Yumer, S. Hadap, K. Sunkavalli, E. Shecht- man, and D. Samaras, “Neural face editing with intrinsic image disentangling,” in CVPR. IEEE, 2017, pp. –

work page 2017

[10] [10]

Context encoders: Feature learning by in- painting,

D. Pathak, P. Kr ¨ahenb¨uhl, J. Donahue, T. Darrell, and A. Efros, “Context encoders: Feature learning by in- painting,” in CVPR, 2016

work page 2016

[11] [11]

Improved techniques for training gans,

T. Salimans, I. Goodfellow, W. Zaremba, V . Cheung, A. Radford, and X. Chen, “Improved techniques for training gans,” in NIPS, pp. 2234–2242. 2016

work page 2016

[12] [12]

Pixel-level domain transfer,

D. Yoo, S. Park Kim, N. Kim, A. S. Paek, and I. Kweon, “Pixel-level domain transfer,” in ECCV, 10 2016, vol. 9912, pp. 517–532

work page 2016

[13] [13]

Learning temporal transfor- mations from time-lapse videos,

Y . Zhou and T. L. Berg, “Learning temporal transfor- mations from time-lapse videos,” in ECCV, 2016, pp. 262–277

work page 2016

[14] [14]

Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network,

H. J. Lee, S. T. Kim, H. Lee, and Y . M. Ro, “Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network,” IEEE Transactions on Circuits and Systems for Video Technology, 2019

work page 2019

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Bbc net: Bounding-box critic network for occlusion-robust object detection,

J. U. Kim, J. Kwon, H. G. Kim, and Y . M. Ro, “Bbc net: Bounding-box critic network for occlusion-robust object detection,” IEEE Transactions on Circuits and Systems for Video Technology, 2019

work page 2019

[16] [16]

Stan: Spatio- temporal adversarial networks for abnormal event detec- tion,

S. Lee, H. G. Kim, and Y . M. Ro, “Stan: Spatio- temporal adversarial networks for abnormal event detec- tion,” in 2018 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP) , April 2018, pp. 1323–1327

work page 2018

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Conditional Generative Adversarial Nets

M. Mirza and S. Osindero, “Conditional generative ad- versarial nets,” CoRR, vol. abs/1411.1784, 2014

work page internal anchor Pith review Pith/arXiv arXiv 2014

[18] [18]

Learning structured output representation using deep conditional generative models,

K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” in NIPS, pp. 3483–3491. 2015

work page 2015

[19] [19]

Deformable gans for pose-based human image genera- tion,

A. Siarohin, E. Sangineto, S. Lathuilire, and N. Sebeu, “Deformable gans for pose-based human image genera- tion,” in CVPR, June 2018

work page 2018

[20] [20]

Pose guided person image generation,

L. Ma, X. Jia, Q. Sun, B. Schiele, T. Tuytelaars, and L. Van Gool, “Pose guided person image generation,” in NIPS, 2017, pp. 405–415

work page 2017

[21] [21]

Disentangled person image generation,

L. Ma, Q. Sun, S. Georgoulis, L. V . Gool, B. Schiele, and M. Fritz, “Disentangled person image generation,” in CVPR, 2018

work page 2018

[22] [22]

Multi-view image generation from a single-view,

B. Zhao, X. Wu, Z. Cheng, H. Liu, Z. Jie, and J. Feng, “Multi-view image generation from a single-view,” in Proceedings of the 26th ACM International Conference on Multimedia, 2018, pp. 383–391

work page 2018

[23] [23]

Dense pose transfer,

N. Neverova, R. Alp Guler, and I. Kokkinos, “Dense pose transfer,” in ECCV, 2018

work page 2018

[24] [24]

U-net: Convo- lutional networks for biomedical image segmentation,

O. Ronneberger, P.Fischer, and T. Brox, “U-net: Convo- lutional networks for biomedical image segmentation,” in MICCAI, 2015, vol. 9351 of LNCS, pp. 234–241

work page 2015

[25] [25]

Photo-realistic fa- cial emotion synthesis using multi-level critic networks with multi-level generative model,

M. Park, H. G. Kim, and Y . M. Ro, “Photo-realistic fa- cial emotion synthesis using multi-level critic networks with multi-level generative model,” inMultiMedia Mod- eling, Cham, 2019, pp. 3–15, Springer International Publishing

work page 2019

[26] [26]

Deep- fashion: Powering robust clothes recognition and re- trieval with rich annotations.,

Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang, “Deep- fashion: Powering robust clothes recognition and re- trieval with rich annotations.,” in CVPR, 2016, pp. 1096–1104

work page 2016

[27] [27]

Real- time multi-person 2d pose estimation using part afﬁnity ﬁelds,

Z. Cao, T. Simon, S. Wei, and Y . Sheikh, “Real- time multi-person 2d pose estimation using part afﬁnity ﬁelds,” in CVPR, 2017, vol. 00, pp. 1302–1310

work page 2017

[28] [28]

Adam: A Method for Stochastic Optimization

D. Kingma and J. Ba, “Adam: A method for stochastic optimization.,” CoRR, vol. abs/1412.6980, 2014

work page internal anchor Pith review Pith/arXiv arXiv 2014

[29] [29]

Im- age quality assessment: from error visibility to struc- tural similarity.,

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Im- age quality assessment: from error visibility to struc- tural similarity.,” IEEE Trans. Image Processing , vol. 13, no. 4, pp. 600–612, 2004

work page 2004