A Review of Human Emotion Synthesis Based on Generative Technology

Fei Ma; Fei Richard Yu; Fuji Ren; Hongwei Ren; Shiguang Ni; Wei Yao; Yifan Xie; Ying He; Yi Zhang; Yukan Li

REVIEW 4 major objections 6 minor 1 cited by

A Review of Human Emotion Synthesis Based on Generative Technology

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read This paper claims to be the first systematic overview of human emotion synthesis based on generative technology, organizing more than 230 papers into a taxonomy across facial images, speech, and text, and arguing that diffusion models…

desk verdict A useful multi-modal survey whose taxonomy and tables earn it referee time, but the 'first systematic overview' claim and the unauditable selection procedure need fixing before it can be relied on. read the letter →

arxiv 2412.07116 v1 pith:L63UYAFN submitted 2024-12-10 cs.LG cs.AIcs.CL

Fei Ma , Yukan Li , Yifan Xie , Ying He , Yi Zhang , Hongwei Ren , Zhou Liu , Wei Yao

show 3 more authors

Fuji Ren Fei Richard Yu Shiguang Ni

This is my paper · ORCID

classification cs.LGcs.AIcs.CL

keywords emotionsynthesisgenerativemodelsadversarialnetworksdiffusionlargelanguageaffectivecomputingfacialtexttransfer

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

This paper is a systematic review of how generative models produce human emotion in three modalities: facial images, speech, and text. It claims to be the first overview spanning all three modalities under one taxonomy and covering five generative families: autoencoders, GANs, diffusion models, large language models, and sequence-to-sequence models. The review catalogs more than 230 papers, summarizes the main datasets and evaluation metrics, and states a set of field-level findings. The central qualitative claim is that GANs have historically dominated facial emotion synthesis but diffusion models now look like the more promising alternative, while LLMs and Seq2Seq models carry textual emotion synthesis. If this map is right, researchers gain a reliable picture of what has been tried and where the field is moving.

What carries the argument

The central object is the taxonomy in Fig. 2, a three-modality by five-model-family grid that organizes every surveyed paper into a cell. The taxonomy is what makes the review's comparative findings possible: the claim that diffusion models are becoming more promising than GANs for facial emotion synthesis, for example, is read from the distribution of recent diffusion entries in the face reenactment, talking head, and face manipulation rows. The dataset table and the metric tables work as auxiliary machinery that lets a reader check whether a given trend claim rests on common benchmarks and comparable evaluation.

What would settle it

Re-run the stated queries on IEEE Xplore, ScienceDirect, and Google Scholar for 2017-2024 and record a documented exclusion flow; if the resulting pool differs materially from the 230+ papers in Fig. 2 in the per-modality distribution of model families, especially the share of diffusion models in facial emotion synthesis, then the taxonomy and the main trend claim do not hold.

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Extended reading notes

Core claim

On its own terms, the paper's contribution is a claim of scope and structure: it is the first systematic review of human emotion synthesis built on generative technology, and its organizing taxonomy is the right way to see the field. The paper groups facial emotion synthesis into face reenactment, face manipulation, and talking head generation; speech emotion synthesis into voice conversion, text-to-speech, and speech manipulation; and textual emotion synthesis into text emotion transfer and empathetic dialogue generation. Against that grid it arrays roughly 230 papers and reads off trends: diffusion models are emerging as a more promising alternative to GANs for facial emotions; speech synthesis is carried by GAN and Seq2Seq adaptation with AE and DM refinements; and textual emotion synthesis increasingly leans on LLMs and Seq2Seq architectures. The review also asserts that credible evaluation requires both subjective human scoring and objective metrics, and that future progress will come from hybrid model combinations, new modalities, and real-time edge deployment.

Load-bearing premise

The review's completeness and its main trend finding depend on the Section 2 search being unbiased, but the selection steps are not auditable: per-stage exclusion counts are missing, the filter against 'incremental contributions' is subjective, and the stated peer-review criterion contradicts the presence of arXiv preprints.

Editorial extensions

If this is right

A newcomer can locate the dominant model family for any emotion-synthesis sub-task through the taxonomy in Fig. 2 rather than searching from scratch.
The finding that diffusion models outperform GANs in facial emotion synthesis, if correct, predicts that new facial emotion work will increasingly be diffusion-based and that GAN-only baselines will be compared against DM alternatives.
LLM- and Seq2Seq-based text emotion synthesis is mature enough to be a distinct sub-field with its own benchmarks, such as EmpatheticDialogues and the YELP review corpus, which the review's dataset table documents.
The review's account of evaluation metrics implies that no single metric captures emotional authenticity, which is why future work should continue combining classifier accuracy, structural or pitch metrics, and human scoring.

Reading between the lines

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

A consequence the authors leave implicit: if the taxonomy is accurate, cross-modal emotion synthesis systems can be assembled by slotting a proven DM-based face module and an LLM-based text module under a shared emotion-label space.
An audit that reproduces Section 2's database queries would test whether the diffusion-model trend survives a more inclusive search that does not filter out 'incremental' papers.
The review's many arXiv preprints despite its stated peer-review criterion suggest that the field's fast-moving results live outside traditional venues; a follow-up review might deliberately track preprints as a separate stratum.
A testable extension: assign each paper in Tables 5 to 7 a publication year and model family, then plot the family share over time; the diffusion-upward, GAN-downward trajectory for faces could be quantified rather than asserted.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Punchline: this is a genuinely useful survey that does not quite support its own firstness claim. If you need a map of generative emotion synthesis across face, speech, and text, this is one of the few single documents spanning AE/GAN/DM/LLM/Seq2Seq, and the dataset and metric tables (Tables 4 and 8) are reference-grade. The taxonomy in Fig. 2 is sensible, and the paper is honest in Table 2 about prior modality-specific and GAN-only reviews.

What is actually new: the multi-family, multi-modality span and 2017-2024 coverage, organized into sub-tasks like face reenactment, talking head generation, voice conversion, TTS, text emotion transfer, and empathetic dialogue. That organizational work has real value for newcomers and for people scoping research. The mathematical primers and evaluation-metric summaries are standard but well executed.

Soft spots, in proportion: the methodology section is the weak load-bearing piece. The inclusion criterion says 'peer-reviewed papers,' yet the reference list is full of arXiv preprints (e.g., [23], [146], [166], [210], [225]). The per-stage exclusion counts are not reported, and the 'eliminated papers with incremental contributions' filter is subjective, so the reduction from more than 270 papers to roughly 230 cannot be audited. The stress-test note is right that this could tilt the diffusion-model trend finding in Section 11.1, because well-known model families are more likely to survive a vague 'incremental' filter. That said, the trend itself is plausible and consistent with what I see in the literature; the problem is evidentiary, not necessarily factual.

The assembly errors are minor but real: a duplicated ExprGAN row in Table 5, a malformed Eq. (6), and a duplicated caption item in Fig. 1. Also, the 'first systematic overview' claim should be softened. Table 2 itself shows a multi-modal GAN review [9], so 'first multi-generative-family overview' is defensible, but 'first systematic' as stated overreaches.

Bottom line: the taxonomy, datasets, and metrics make this worth serious referee time. I would accept it for review, but with a required methodology audit and a copyediting pass. Once it is in shape, I would cite it as a starting map of the field, not as an authority.

Referee Report

4 major / 6 minor

Summary. This manuscript is a survey of generative-model-based human emotion synthesis across facial images, speech, and text. It claims to be the first systematic overview of this area, analyzes more than 230 papers, and organizes them into a taxonomy (Fig. 2) with separate treatment of face reenactment, face manipulation, talking-head generation, voice conversion, text-to-speech, speech manipulation, text emotion transfer, and empathetic dialogue generation. The paper also presents emotion models, mathematical background for five generative model families (AE, GAN, DM, LLM, Seq2Seq), a dataset summary (Table 4), literature tables with reported performance (Tables 5-7), evaluation metrics, a discussion of major findings, and future research directions.

Significance. If the underlying selection and reporting are reliable, this survey is a useful reference for a growing interdisciplinary area. Its strengths include a broad coverage of roughly 230 works, a clear three-modality taxonomy, dataset tables, a comparison with prior reviews (Table 2), and qualitative field-level findings such as the growing role of diffusion models in facial emotion synthesis. The review contains no new experiments or derivations, so its value rests entirely on the completeness and accuracy of its literature selection and on the faithful transcription of performance numbers. The bookkeeping and methodological transparency problems identified below are therefore load-bearing rather than cosmetic.

major comments (4)

[Section 2 (Review Methodology), Fig. 3] The screening process is not auditable. The text reports only that searches in IEEE Xplore, ScienceDirect, and Google Scholar yielded 'more than 270 papers' and that a 'two-step filtering process' was applied, but it gives no per-stage exclusion counts, query strings, search dates, or protocol for title/abstract/full-text screening. Because the central claim is to be the 'first systematic overview,' the absence of an auditable protocol means the taxonomy in Fig. 2 and the coverage claims cannot be checked. Please provide the full search strings, the number of records at each stage, and a reproducible list of inclusion/exclusion decisions.
[Section 2, inclusion criterion (1)] The stated criterion that only 'Peer-reviewed papers published up to November 2024' are included is contradicted by the reference list, which contains many arXiv preprints without a peer-reviewed venue, including [23], [36], [133], [134], [146], [166], [183], [193], [200], [210], [224], and [225]. The manuscript should either restrict the corpus to peer-reviewed versions where they exist or explicitly revise the inclusion criterion to admit preprints and explain their role; as written, the methodology does not describe the actual corpus.
[Section 2, filtering step] The statement that the authors 'eliminated papers with incremental contributions' is a subjective filter that is not operationalized. If applied without clear rules, this filter can systematically favor well-known model families and thereby shape the field-level finding in Section 11.1 that diffusion models are 'a more promising alternative' for facial emotion synthesis. Please define the exclusion rule (for example, based on citation counts, novelty criteria, or reproducibility status) and, ideally, report a sensitivity analysis of the model-family distribution under alternative inclusion rules.
[Tables 5-7] The performance transcriptions are not sufficiently reliable as presented. Table 5 lists Ding et al. [125] ('ExprGAN') twice; several entries in Tables 5-7 report only 'figures' without numeric values (for example, Table 5 entries for [93], [126], and [127]; Table 6 entries for [142], [143], [148], [150], [153], [162], [173], [176], and [179]; Table 7 entries for [184], [196], [217], and [222]); and some entries lack units or clear metric definitions. Since the review's value depends on faithfully summarizing reported performance, please correct the duplicate and either provide the actual reported numbers with units or clearly mark entries as unavailable.

minor comments (6)

[Section 6] The section heading 'Databases' should be 'Datasets' to match the content and Table 4.
[Fig. 1 caption] In the source list, items (2) and (3) both describe a 'speech emotion synthesis' schematic; one of them should presumably refer to the text emotion synthesis schematic.
[Section 7.2] There is a typo in the sentence about reference [108]: 'Kong te al.' should be 'Kong et al.'
[Section 12] In the first sentence, 'genrative models' should be 'generative models'.
[Abstract] The phrase 'this paper aims to address this gap by providing' is redundant immediately after 'there is a notable lack of comprehensive reviews'; the sentence should be streamlined.
[Table 4] Several rows have inconsistent spacing and formatting (for example, 'ETOD [81] 2019 audio 6000 speeches 13'), which makes the table harder to read; please ensure uniform formatting across all rows.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation chain: this is a literature review with no fitted parameters or predictions, so none of its claims reduce to its own inputs; the only self-referential element is a minor, non-load-bearing citation to the authors' companion scoping review.

full rationale

This paper is a survey, not a derivation. It reports no new experiments, fits no parameters, and makes no quantitative predictions, so the classical circularity patterns (self-definitional equations, fitted inputs renamed as predictions, ansatz smuggled via citation) do not apply. The load-bearing content is the Fig. 2 taxonomy, Table 4 dataset summaries, and Tables 5-7 performance transcriptions, all of which are drawn from the cited primary literature and are checkable against external sources. The 'first systematic overview' novelty claim is a negative scope claim supported by the comparison in Section 4 and Table 2 against prior reviews such as [9] and [52]-[57]; it is not derived from the authors' own prior results. The companion scoping review [12] is cited only for background statements about emotion recognition and the broad impact of generative technology, and removing it would not change any taxonomic or trend conclusion. The paper's genuine weaknesses are selection-bias and reproducibility issues in Section 2: the search covers only IEEE Xplore, ScienceDirect, and Google Scholar; per-stage exclusion counts are not reported; the filter eliminating 'papers with incremental contributions' is subjective; and the stated 'peer-reviewed papers' criterion is contradicted by many arXiv preprints in the reference list. These issues could bias the comprehensiveness and the Section 11.1 qualitative finding that diffusion models are more promising, but bias is not circularity. No equation in the paper is equivalent by construction to its input, and no fitted value is relabeled as a prediction. The circularity score is therefore low.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

A review introduces no free parameters and no invented entities. The load-bearing assumptions are methodological: the completeness claim depends on a three-database search with un-itemized exclusions, and the cross-model trend claims depend on the comparability of transcription-level performance numbers across heterogeneous papers.

assumptions (3)

domain assumption The three-modality taxonomy with its sub-task splits (face reenactment, face manipulation, talking head; voice conversion, TTS, speech manipulation; text transfer, empathetic dialogue) is the correct organizing principle for the field.
Section 1 and Fig. 2 impose this structure on all 230 reviewed papers; if the categories misalign with how the community actually organizes the work, the survey's map misrepresents the literature even if each entry is accurate.
domain assumption Discrete emotion models (Ekman basics, Plutchik wheel) and dimensional models (valence-arousal, valence-arousal-dominance) adequately cover the emotion labels used across synthesis tasks.
Section 3 adopts these as the theoretical foundation, and the datasets in Table 4 label emotions within these schemes; the review inherits any limitations of the label taxonomies of the cited datasets.
domain assumption The performance numbers transcribed in Tables 5 to 7 are faithful to the cited papers and comparable across models and datasets.
The Section 11.1 trend findings, such as diffusion models being more promising for facial emotion synthesis, depend on this comparability; several table entries report "figures" instead of values, and datasets differ across rows, so comparisons are only indicative.

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Cite this review

Pith. "Pith review of A Review of Human Emotion Synthesis Based on Generative Technology." pith.science (2026). https://pith.science/paper/L63UYAFN

@misc{pith2026241207116,
  author       = {Pith},
  title        = {Pith review of: A Review of Human Emotion Synthesis Based on Generative Technology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L63UYAFN}},
  note         = {Machine review of arXiv:2412.07116}
}

read the original abstract

Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion Models, Large Language Models, and Sequence-to-Sequence Models, have significantly contributed to the development of this field. However, there is a notable lack of comprehensive reviews in this field. To address this problem, this paper aims to address this gap by providing a thorough and systematic overview of recent advancements in human emotion synthesis based on generative models. Specifically, this review will first present the review methodology, the emotion models involved, the mathematical principles of generative models, and the datasets used. Then, the review covers the application of different generative models to emotion synthesis based on a variety of modalities, including facial images, speech, and text. It also examines mainstream evaluation metrics. Additionally, the review presents some major findings and suggests future research directions, providing a comprehensive understanding of the role of generative technology in the nuanced domain of emotion synthesis.

Figures

Figures reproduced from arXiv: 2412.07116 by the authors.

**Figure 2.** Taxonomy of This Survey. approach was followed to ensure the comprehensiveness and relevance of the literature. The overall screening process is shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Plutchik Wheel (left) and 2D Emotion Model (right). [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

Figures from the paper (7 more)

**Figure 3.** Figure 3: A Comprehensive Review Methodology. up to November 2024, including both journal and conference papers; (2) Research focusing on the application of generative models to human emotion synthesis in various modalities; (3) Studies in which extensive experiments were condu…

**Figure 5.** Figure 5: A mask-based GAN [95] for face reenactment. The system [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: A DM-based talking head generation model from [23]. This archi [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: A two-stage model [145] for voice conversion. In the first stage, [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]

**Figure 8.** Figure 8: A Tacotron2-based TTS model from [164]. This system integrated [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

**Figure 9.** Figure 9: A text emotion transfer model from [183]. During training, the [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

**Figure 10.** Figure 10: A empathetic dialogue generation system from [198]. The [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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Scalable Event Cloud Network for Event-based Classification
cs.CV 2024-12 conditional novelty 6.0 of 10

A frequency-aware network operating on raw-like event clouds matches or beats prior event-based models on nine benchmarks while using roughly 0.1 G MACs, far below frame and voxel baselines.

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