Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

Fang Kang; Haoyu Chen; Yin Cao

REVIEW 6 major objections 5 minor 35 references

Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

T0 review · 6 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read Face2VoiceSync: from one photo and text, generate a talking face with a matching editable voice.

desk verdict The VAE adapter and DCTS metric are worth a look, but the SOTA claim is contradicted by the paper's own Table 1 and the audio identity evaluation is circular. read the letter →

arxiv 2507.19225 v1 pith:OQH35PAH submitted 2025-07-25 cs.SD cs.CVcs.MMeess.AS

Fang Kang , Yin Cao , Haoyu Chen This is my paper · ORCID

classification cs.SDcs.CVcs.MMeess.AS

keywords talkingfacegenerationface-to-voicemappingtext-to-speechsynthesisvariationalautoencodercross-modalalignmentidentityconsistencyvoicediversityDCTS

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

Face2VoiceSync sets out to solve a harder version of talking-face generation: from a single face photo and a line of text, produce both a realistically moving face video and a spoken voice that plausibly belongs to that face. Most existing systems are audio-driven and therefore impose a fixed, pre-recorded voice on the face, while text-driven alternatives usually assume a one-to-one face-to-voice mapping. The paper instead models the mapping as a probability distribution over voices conditioned on facial appearance, and bridges frozen video and speech generators with a small trainable variational autoencoder called VoiceAdapter. If the approach works as claimed, a single image and text could drive low-cost, editable, identity-consistent avatars without reference audio, and the new DCTS metric would give the field a way to score the diversity-versus-identity tradeoff.

What carries the argument

VoiceAdapter: a two-linear-layer VAE inserted between a frozen video generator and a frozen TTS model. It encodes face features $v$ into a latent distribution $q(z|v)$, regularizes the posterior toward a Gaussian prior with maximum mean discrepancy, and decodes samples $z$ into identity embeddings $s$ that condition the TTS model; it is trained first on embedding losses (reconstruction, contrastive, center) and then on generative losses (TTS speaker-embedding similarity and speech-representation feature alignment). The key work of this object is converting a one-to-one face-to-voice assumption into a many-to-many distributional mapping while keeping all large pretrained components frozen.

What would settle it

Build a test set of faces whose voices are deliberately mismatched, such as identical twins with distinct voices or face-voice pairs shuffled at training time, and measure DCTS plus human judgments of face-voice believability; if DCTS stays near the matched-pair level, the metric is not capturing the face-voice link, and if human ratings do not track DCTS, the claimed identity consistency is not perceptually real.

Watch

Extended reading notes

Core claim

The central discovery is that face-to-voice consistency in text-driven talking face generation can be achieved by a distributional adapter rather than by joint training or a fixed regression. The paper proposes VoiceAdapter, a lightweight VAE that maps face-image embeddings from a frozen video generator into identity embeddings for a frozen TTS model, learning $p(s|v)$ instead of a point mapping. Sampling from the learned distribution produces diverse but identity-consistent voices for the same face, and two-stage training with reconstruction, contrastive, center, TTS, and speech-representation losses keeps the embeddings compatible with both frozen backbones. The paper's tables report audio results that beat the compared TTS baselines in word error rate and video results comparable to audio-driven generators; the abstract frames this as state-of-the-art performance in both modalities. It also introduces DCTS, a normalized combination of a relative independence ratio and a relative cosine ratio, to measure the consistency-diversity tradeoff.

Load-bearing premise

The load-bearing premise is that a person's facial appearance carries enough information to predict their voice characteristics; if faces and voices are only weakly linked, the adapter can learn the training distribution but the generated voice will not reliably match the face.

Editorial extensions

If this is right

Text-driven avatars no longer need reference audio, removing the main source of face-voice mismatch in current pipelines.
A single face can yield multiple sampled voice styles, enabling user control over paralinguistic traits without retraining.
Because only the small adapter is trained, full pipelines become cheaper to train and can adapt zero-shot to unseen faces.
DCTS offers a standardized number for the diversity-consistency tradeoff, applicable beyond the specific generators used here.
The distributional mapping suggests the same adapter idea could extend to other many-to-many generative alignments, such as identity-to-motion or emotion-to-speech.

Reading between the lines

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

I infer that the sampling mechanism could be turned into an explicit control knob: adjusting the latent prior variance or sampling temperature would let users trade voice diversity against identity strictness, a control the paper does not implement.
The DCTS ratio construction is general enough that it could be applied to evaluate other generative many-to-many tasks, though the paper only demonstrates it on face-to-voice mapping.
A natural test the paper does not report is human listening judgment; correlating DCTS with listener ratings of face-voice believability would show whether the metric captures perceptual identity.
Because the method inherits its ceiling from the frozen video and TTS backbones, replacing those components with stronger generators should improve output quality without adapter retraining, assuming the embedding spaces remain compatible.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Colleague,

Short version: Face2VoiceSync is a reasonable engineering contribution, but the paper oversells it. The abstract claims visual and audio SOTA; Table 1 shows the method is worse than Hallo on FID, FVD, Sync-C, and Sync-D. The text later says "comparable," which is accurate, but the abstract and conclusion still say SOTA. That needs fixing.

What is genuinely new: the VoiceAdapter is a small VAE that maps face embeddings to a distribution over speaker embeddings, so one face can yield multiple voice styles. That is a sensible way to handle the many-to-many face-voice relationship, and the two-stage training (embedding learning then generative pretraining) is a reasonable way to bridge frozen TTS and video models. The DCTS metric is also new, and the idea of trading off diversity against consistency is worth discussing. Training on a single 40GB GPU with few trainable parameters is nice if the claim is quantified. The ablation suggests the VAE and LGen loss both help.

Where it falls short. The most serious issue is the circular evaluation: the VoiceAdapter is trained to match Cam++ speaker embeddings (Eq. 2), and the SS and DCTS metrics in Section 4.2 are computed on Cam++ embeddings as well. Measuring identity consistency in the training space is self-referential; it does not tell you whether voices sound like the face to a listener. The paper needs at least one external metric, e.g., a different speaker encoder or human evaluation. The DCTS metric is also defined on those same embeddings, so it inherits the bias.

Second, the missing baselines matter. Faces That Speak [7] and FVTTS [20] are cited in related work but not compared. Those are exactly the text-driven methods this paper extends, so the audio SOTA claim has no direct support.

Third, the contribution list overclaims. "Significantly fewer trainable parameters" is never quantified. The "diversity & manipulation" claim is not demonstrated beyond the VAE sampling; there is no experiment showing a user can actually control the paralinguistic space.

What I would not do is dismiss the method. The architecture is coherent, the losses make sense, and the ablation is honest about small gains. The paper's own numbers are not catastrophic; the problem is framing, not the core idea. The face-to-voice distribution assumption is a reasonable prior, even if it is not validated against twins or ambiguous faces.

Who this is for: people working on joint speech-and-face generation will want to know about the VAE adapter and DCTS. It is an incremental but potentially useful contribution.

Recommendation: send to peer review, but require the authors to correct the SOTA claims, add the missing baselines, and include a non-circular identity evaluation. With those changes, this could be a solid paper.

Referee Report

6 major / 5 minor

Summary. The paper addresses text-driven talking face generation: from a single face image and a text sentence, the system synthesizes both a talking-face video and matching speech. The proposed Face2VoiceSync pipeline freezes CosyVoice as the text-to-speech generator and Hallo as the audio-to-video generator, and inserts a lightweight VAE-based VoiceAdapter that maps face-encoder features to speaker identity embeddings used by CosyVoice. Training uses reconstruction, contrastive, center, MMD, and generation losses (Eqs. 2 and 5). The paper also proposes a new metric, DCTS, for assessing the trade-off between identity consistency and voice diversity. Experiments are reported on LRS2 and HDTF, comparing against Hallo and SadTalker for video generation and CosyVoice and FaceTTS for speech, with ablations in Table 3.

Significance. The architecture is potentially useful: reusing frozen large pretrained models with a small trainable adapter is a sensible way to lower training cost for multimodal generation, and modeling face-to-voice mapping as a stochastic conditional distribution is a reasonable step beyond fixed one-to-one mappings. The proposed DCTS metric is also an interesting attempt to quantify diversity and identity consistency jointly. However, the current evidence does not support the state-of-the-art claim, and the audio evaluation is partially self-referential because the training target and the evaluation embedding space coincide. If the claims are corrected and the evaluation is strengthened, this could become a solid workshop- or conference-level contribution, but major revision is needed first.

major comments (6)

[Abstract and Section 4.2, Table 1] The abstract and Section 5 claim "both visual and audio state-of-the-art performances," but Table 1 shows that on every reported TFG metric the proposed method is worse than the Hallo baseline: FID 24.272 vs. 20.545, FVD 194.462 vs. 173.497, Sync-C 7.605 vs. 7.750, and Sync-D 7.876 vs. 7.659. The text in Section 4.2 itself only claims performance "comparable to audio-driven approaches," which contradicts the abstract. The SOTA claim is not supported and should be removed or replaced with a precisely scoped statement.
[Section 4.1 and Table 1] The comparison in Table 1 is not apples-to-apples. Hallo and SadTalker are audio-driven methods, while Face2VoiceSync is text-driven and its video generator is driven by audio synthesized by CosyVoice. Section 4.1 states that "all the compared methods are tested on the same audio samples for audio-driven testing," but if Hallo and SadTalker receive ground-truth audio while Ours receives TTS-synthesized audio, lower lip-sync scores for Ours are expected and do not indicate worse visual generation. The paper should either compare all methods under the same audio condition or re-run audio-driven baselines on the same generated audio.
[Section 3.2, Eq. (2), and Section 4.2] The audio identity evaluation is circular. The VoiceAdapter is trained to match speaker embeddings produced by the Cam++ speaker encoder [22], as described in Eq. (2) and the surrounding text in Section 3.2. Section 4.2 and Section 3.3 then compute Speaker Similarity and DCTS using the same "speaker encoder [22]" on the generated speech. The model is therefore evaluated in the exact embedding space it was optimized to match. To support the face-voice consistency claim, the authors should evaluate with a held-out speaker encoder not used in training, or add human listener judgments of voice-identity match.
[Section 4.2, Tables 2 and 3] The TTS comparison omits the closest text-driven baselines cited in the related work, namely Faces That Speak [7] and FVTTS [20]. These methods also generate speech from text with face conditioning, so without them the claims of audio state-of-the-art performance and improved face-voice consistency are not established. The comparison against CosyVoice and FaceTTS is useful but insufficient.
[Section 3.3, Eqs. (6)-(9)] The DCTS metric is underspecified. The paper does not state how the intra-class and inter-class sample sets are constructed, how the joint and marginal densities in Eq. (7) are estimated beyond "KDE," what value of K is used, how normalization of RIR_m and RCR_m is performed, or how the metric behaves under trivial choices such as all samples from one class. Since DCTS is one of the paper's main evaluation instruments, it must be fully specified and ideally validated against human perceptual judgments.
[Abstract and Section 1] The contribution list promises "voice control over paralinguistic features space," but no experiment in Section 4 demonstrates manipulation or control of paralinguistic attributes (e.g., pitch, speaking rate, emotion). Diversity is only characterized statistically through DCTS, not through controlled generation. This claim should be either demonstrated or explicitly deferred to future work.

minor comments (5)

[Section 1 and abstract] The phrase "significantly fewer trainable parameters than existing methods" is never quantified. A table with parameter counts of the trainable modules and the full pipelines is needed to substantiate the efficiency claim.
[Section 3.2, Eq. (2)] The loss weights lambda_Rec, lambda_Con, lambda_cen, lambda_tts, and lambda_vec are never given, and no sensitivity analysis of these weights is reported. Please provide the chosen values, and ideally an ablation on at least one weight.
[Section 4.1] Experimental details are incomplete: the number of training steps, batch size, the exact dataset splits for LRS2, and the hyperparameters for DCTS estimation (e.g., KDE bandwidth) are missing.
[Table 2] The confidence intervals or significance tests for the differences in WER, SS, and DCTS are not reported. Several differences are small (e.g., SS 0.669 vs. 0.643), and without variance estimates it is hard to assess whether the differences are meaningful.
[Throughout] There are several typographical and formatting issues, including the recurring "V oiceAdapter" spacing artifacts, "Few efforts have been made" in the introduction, and the incomplete sentence in the Section 4.2 TTS paragraph. A careful proofread is needed.

Circularity Check

1 steps flagged · score 5.0 of 10

Identity-consistency metrics use the same Cam++ speaker encoder that supplies the training target, making the audio-SOTA sub-claim partially self-referential; the visual-SOTA claim is contradicted by the paper's own Table 1.

fitted input called prediction [Section 3.2 Eq. (2); Section 4.1 DCTS setup; Section 4.2 Table 2]
"target speaker embeddings come from the TTS speaker encoder [22]. ... The reconstruction loss LRec is implemented using cosine similarity to align identity embeddings with the target speaker embedding. ... DCTS computation is applied to the speaker embeddings generated by the speaker encoder [22]."

VoiceAdapter's training objective (Eq. 2) optimizes cosine similarity between predicted identity embeddings and target speaker embeddings extracted by Cam++ [22]. The identity-consistency metrics used to support the audio state-of-the-art claim, especially DCTS and the related speaker-similarity evaluation, are then computed on the same Cam++ [22] speaker-embedding space. Therefore the evaluation measures exactly the quantity optimized during training: closeness in Cam++ embedding space. This does not independently validate face-voice identity consistency; it confirms that the model reproduces its training target. WER provides some external validation of intelligibility, but the identity-consistency component of the audio-SOTA claim reduces to same-space evaluation.

full rationale

The main construction is not circular: VoiceAdapter is a lightweight VAE with MMD regularization and the losses in Eqs. (2) and (5), while the video and TTS backbones are frozen pretrained models, so the framework itself is an independent engineering contribution. However, the paper's identity-consistency evaluation is partially self-referential. The training target in Eq. (2) is the Cam++ speaker embedding [22], and LRec is implemented as cosine similarity to that exact target. Then DCTS, the paper's new metric, is explicitly computed on speaker embeddings produced by the same Cam++ encoder [22]. Measuring face-voice consistency in the same representation space that was optimized during training mostly confirms that the model can match its training objective, not that voice identity is independently verified. This affects the audio state-of-the-art claim, which relies on SS and DCTS. The visual state-of-the-art claim is contradicted by the paper's own Table 1, where Ours has worse FID, FVD, Sync-C, and Sync-D than Hallo; that is a correctness inconsistency rather than a circularity. Reference [24] is a self-citation by the first author, but it only supports a standard speaker-independence assumption and is not load-bearing. No uniqueness theorem or ansatz-smuggling via citation is present. Overall, the central derivation is independent, but the identity-consistency evaluation has a partial same-encoder circularity, giving a score of 5.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method depends on several domain assumptions about face-voice predictability and the sufficiency of a small adapter. The free parameters are mostly standard hyperparameters, but several are unreported. The DCTS metric introduces new constructs (RIR, RCR) rather than new physical entities.

free parameters (5)

VAE latent dimension = 192
The dimension of the identity embedding z, chosen by hand. It determines the expressiveness of the VAE.
Loss weights lambda_Rec, lambda_Con, lambda_cen, lambda_tts, lambda_vec = not reported
Balancing coefficients in Eqs. (2) and (5). Not specified in the paper, so the exact training objective is not reproducible.
Center loss update rate alpha = not reported
Controls how class centers are updated in Eq. (4). Not specified.
Learning rate = 5e-5
Adam learning rate reported in Section 4.1.
Epsilon in DCTS = not reported
Small constant in Eqs. (6) and (8) to avoid division by zero. Not specified.

assumptions (4)

domain assumption Facial appearance predicts voice characteristics.
The entire VoiceAdapter rests on modeling p(s|v) from face embeddings v to speaker embeddings s. If this mapping does not exist in the data, the method cannot work. Introduced in Section 3.2.
domain assumption A lightweight VAE can bridge the latent spaces of frozen Hallo and CosyVoice without degrading their performance.
The paper assumes that a 2-layer VAE is sufficient to align the two frozen models. This is an architectural bet that is not derived or proven.
domain assumption The independence assumption from blind speech separation applies to speaker embeddings.
DCTS relies on the idea that different speakers are independent and intra-speaker samples are dependent, citing [24]. This is transferred to the embedding space without validation.
domain assumption Kernel Density Estimation (KDE) with the available samples yields reliable independence estimates for RIR.
DCTS uses KDE to estimate joint and marginal densities in Eq. (7). In high-dimensional embeddings with limited test samples, KDE is known to be unreliable, yet the paper only tests with GMM as an alternative.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation." pith.science (2026). https://pith.science/paper/OQH35PAH

@misc{pith2026250719225,
  author       = {Pith},
  title        = {Pith review of: Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OQH35PAH}},
  note         = {Machine review of arXiv:2507.19225}
}

read the original abstract

Recent studies in speech-driven talking face generation achieve promising results, but their reliance on fixed-driven speech limits further applications (e.g., face-voice mismatch). Thus, we extend the task to a more challenging setting: given a face image and text to speak, generating both talking face animation and its corresponding speeches. Accordingly, we propose a novel framework, Face2VoiceSync, with several novel contributions: 1) Voice-Face Alignment, ensuring generated voices match facial appearance; 2) Diversity \& Manipulation, enabling generated voice control over paralinguistic features space; 3) Efficient Training, using a lightweight VAE to bridge visual and audio large-pretrained models, with significantly fewer trainable parameters than existing methods; 4) New Evaluation Metric, fairly assessing the diversity and identity consistency. Experiments show Face2VoiceSync achieves both visual and audio state-of-the-art performances on a single 40GB GPU.

Figures

Figures reproduced from arXiv: 2507.19225 by the authors.

**Figure 1.** A framework comparison between Face2VoiceSync and other existing methods. tage over audio-driven approaches, which may struggle to fully align visual and auditory features. On the other hand, current methods [7, 8, 12] frequently conceptualize the face-voice relationship as a one-to-one mapping, aiming to establish a direct correlation between facial features and voice attributes to improve the naturalness of gene… view at source ↗

**Figure 2.** Framework of the proposed text-driven talking face generation system. The framework takes both text and image inputs, leveraging VoiceAdapter to bridge the audio-to-video generator and text-to-speech generator. Dashed lines represent data flow paths that are only active during the training phase. pre-trained video generators and large-scale TTS models. In the following, we introduce each components in the framework … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 22 canonical work pages

[7]

Faces that speak: Jointly syn- thesising talking face and speech from text,

Y . Jang, J.-H. Kim, J. Ahn, D. Kwak, H.-S. Yang, Y .-C. Ju, I.-H. Kim, B.-Y . Kim, and J. S. Chung, “Faces that speak: Jointly syn- thesising talking face and speech from text,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, 2024, pp. 8818–8828

work page 2024
[20]

Generating talking face with controllable eye movements by disentangled blinking feature,

S. Liu and J. Hao, “Generating talking face with controllable eye movements by disentangled blinking feature,” IEEE Transactions on Visualization and Computer Graphics , vol. 29, no. 12, pp. 5050–5061, 2022

work page 2022
[22]

A lip sync expert is all you need for speech to lip generation in the wild,

K. Prajwal, R. Mukhopadhyay, V . P. Namboodiri, and C. Jawahar, “A lip sync expert is all you need for speech to lip generation in the wild,” in Proceedings of the 28th ACM international conference on multimedia, 2020, pp. 484–492

2020
[1]

Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

Introduction Talking Face Generation (TFG) aims to synthesize realistic fa- cial animations synchronized with speech, enabling applica- tions in virtual avatars, digital assistants, and media produc- tion. Recent advancements, particularly diffusion-based meth- ods, have significantly improved lip-sync accuracy and facial expressiveness, drawing increasin...

work page Pith review arXiv 2025
[2]

Early works, such as Chen et al

Related works Talking face generation (TFG) has predominantly utilized audio-driven methods to create speaking portraits from a static image and audio input. Early works, such as Chen et al. [14] and Prajwal et al. [15] convert speech into mouth movements, laying the foundation for TFG. SadTalker [2] improved realism using implicit 3D coefficient modulati...

work page
[3]

The overall pipeline is illustrated in Figure 2

Method We propose Face2V oiceSync, a scalable text-driven talking face generation method. The overall pipeline is illustrated in Figure 2. In Section 3.1, we provide an overview of the Face2V oiceSync framework. Section 3.2 details V oiceAdapter and its training strategies. Finally, Section 3.3 introduces DCTS, our proposed evaluation metric. 3.1. Face2Vo...

work page
[4]

Experimental Settings We conduct our experiments using LRS2 [25] and HDTF [26] datasets

Experiments 4.1. Experimental Settings We conduct our experiments using LRS2 [25] and HDTF [26] datasets. The LRS2 dataset contains talking face videos paired with transcription labels. We split it into 3,347 speakers for training and 318 speakers for testing. HDTF consists of high- quality YouTube videos with more than 300 speakers, which we use for test...

work page
[5]

Unlike prior works assuming a fixed face-to-voice mapping, we model it as a probability distribution problem, capturing natural voice variability

Conclusion We propose Face2V oiceSync, a scalable text-driven talking face generation method that synthesizes natural, identity-consistent animation and speech from a single image and text. Unlike prior works assuming a fixed face-to-voice mapping, we model it as a probability distribution problem, capturing natural voice variability. A lightweight V AE b...

work page

Show all 35 references

[6]

The authors also acknowledge CSC-IT Center for Science, Finland, for pro- viding computational resources

Acknowledgements This work was supported by the University of Oulu and the Re- search Council of Finland (PROFI7 grant 352788). The authors also acknowledge CSC-IT Center for Science, Finland, for pro- viding computational resources
[8]

Audio2head: Audio- driven one-shot talking-head generation with natural head mo- tion,

S. Wang, L. Li, Y . Ding, C. Fan, and X. Yu, “Audio2head: Audio- driven one-shot talking-head generation with natural head mo- tion,” arXiv preprint arXiv:2107.09293, 2021

2021 arXiv
[9]

Sadtalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,

W. Zhang, X. Cun, X. Wang, Y . Zhang, X. Shen, Y . Guo, Y . Shan, and F. Wang, “Sadtalker: Learning realistic 3d motion coefficients for stylized audio-driven single image talking face animation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni...

2023
[10]

Dreamtalk: When expressive talking head generation meets dif- fusion probabilistic models,

Y . Ma, S. Zhang, J. Wang, X. Wang, Y . Zhang, and Z. Deng, “Dreamtalk: When expressive talking head generation meets dif- fusion probabilistic models,” arXiv preprint arXiv:2312.09767 , 2023

2023 arXiv
[11]

Hallo: Hierarchical audio-driven visual synthesis for portrait image animation,

M. Xu, H. Li, Q. Su, H. Shang, L. Zhang, C. Liu, J. Wang, Y . Yao, and S. Zhu, “Hallo: Hierarchical audio-driven visual synthesis for portrait image animation,” arXiv preprint arXiv:2406.08801, 2024

2024 arXiv
[12]

Edtalk: Efficient disentangle- ment for emotional talking head synthesis,

S. Tan, B. Ji, M. Bi, and Y . Pan, “Edtalk: Efficient disentangle- ment for emotional talking head synthesis,” in European Confer- ence on Computer Vision. Springer, 2024, pp. 398–416

2024
[13]

Text2video: Text- driven talking-head video synthesis with personalized phoneme- pose dictionary,

S. Zhang, J. Yuan, M. Liao, and L. Zhang, “Text2video: Text- driven talking-head video synthesis with personalized phoneme- pose dictionary,” in ICASSP 2022-2022 IEEE International Con- ference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 2659–2663

2022
[14]

Hierarchical cross- modal talking face generation with dynamic pixel-wise loss,

L. Chen, R. K. Maddox, Z. Duan, and C. Xu, “Hierarchical cross- modal talking face generation with dynamic pixel-wise loss,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 7832–7841

2019
[15]

Text-to-video: a two-stage framework for zero-shot identity-agnostic talking-head genera- tion,

Z. Wang, M. Dai, and K. Lundgaard, “Text-to-video: a two-stage framework for zero-shot identity-agnostic talking-head genera- tion,” arXiv preprint arXiv:2308.06457, 2023

2023 arXiv
[16]

Ada-tta: Towards adaptive high-quality text-to-talking avatar synthesis,

Z. Ye, Z. Jiang, Y . Ren, J. Liu, C. Zhang, X. Yin, Z. Ma, and Z. Zhao, “Ada-tta: Towards adaptive high-quality text-to-talking avatar synthesis,” arXiv preprint arXiv:2306.03504, 2023

2023 arXiv
[17]

Uniflg: Unified facial land- mark generator from text or speech,

K. Mitsui, Y . Hono, and K. Sawada, “Uniflg: Unified facial land- mark generator from text or speech,” in Proceedings of the 24th Annual Conference of the International Speech Communication Association (INTERSPEECH), 2023, pp. 5501–5505

2023
[18]

The results are presented in Table 2

and FaceTTS [19] on the LRS2 dataset using: Word Error Rate (WER) and Speaker Similarity (SS) from [18] (ASR-based and speaker embedding similarity), and our proposed DCTS metric with sub-metrics RIR m and RCR m. The results are presented in Table 2. Our method achieves the lo...
[19]

Text-driven talk- ing face synthesis by reprogramming audio-driven models,

J. Choi, M. Kim, S. J. Park, and Y . M. Ro, “Text-driven talk- ing face synthesis by reprogramming audio-driven models,” in ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024, pp. 8065– 8069

2024
[21]

Ft2tf: First- person statement text-to-talking face generation,

X. Diao, M. Cheng, W. Barrios, and S. Jin, “Ft2tf: First- person statement text-to-talking face generation,” arXiv preprint arXiv:2312.05430, 2023

2023 arXiv
[23]

GAIA: Zero-shot talking avatar gener- ation,

T. He, J. Guo, R. Yu, Y . Wang, J. Zhu, K. An, L. Li, X. Tan, C. Wang, H. Hu et al., “GAIA: Zero-shot talking avatar gener- ation,” in International Conference on Learning Representations (ICLR)
[24]

Por- traittalk: Towards customizable one-shot audio-to-talking face generation,

F. Nazarieh, Z. Feng, D. Kanojia, M. Awais, and J. Kittler, “Por- traittalk: Towards customizable one-shot audio-to-talking face generation,” arXiv preprint arXiv:2412.07754, 2024

2024
[25]

Cosyvoice 2: Scalable stream- ing speech synthesis with large language models,

Z. Du, Y . Wang, Q. Chen, X. Shi, X. Lv, T. Zhao, Z. Gao, Y . Yang, C. Gao, H. Wanget al., “Cosyvoice 2: Scalable stream- ing speech synthesis with large language models,” arXiv preprint arXiv:2412.10117, 2024

2024 arXiv
[26]

Imaginary voice: Face- styled diffusion model for text-to-speech,

J. Lee, J. S. Chung, and S.-W. Chung, “Imaginary voice: Face- styled diffusion model for text-to-speech,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023, pp. 1–5

2023
[27]

Fvtts: Face based voice synthesis for text-to-speech,

M. Lee, E. Park, and S. Hong, “Fvtts: Face based voice synthesis for text-to-speech,” inProceedings of the 24th Annual Conference of the International Speech Communication Association (INTER- SPEECH), 2024, pp. 4953–4957

2024
[28]

A kernel two-sample test,

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Sch ¨olkopf, and A. Smola, “A kernel two-sample test,” The Journal of Machine Learning Research, vol. 13, no. 1, pp. 723–773, 2012

2012
[29]

Cam++: A fast and efficient network for speaker verification using context- aware masking,

H. Wang, S. Zheng, Y . Chen, L. Cheng, and Q. Chen, “Cam++: A fast and efficient network for speaker verification using context- aware masking,” arXiv preprint arXiv:2303.00332, 2023

2023 arXiv
[30]

wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

A. Baevski, Y . Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 33, 2020, pp. 12 449–12 460

2020
[31]

A low-complexity permutation alignment method for frequency-domain blind source separation,

F. Kang, F. Yang, and J. Yang, “A low-complexity permutation alignment method for frequency-domain blind source separation,” Speech Communication, vol. 115, pp. 88–94, 2019

2019
[32]

Lip read- ing sentences in the wild,

J. Son Chung, A. Senior, O. Vinyals, and A. Zisserman, “Lip read- ing sentences in the wild,” in Proceedings of the IEEE confer- ence on computer vision and pattern recognition, 2017, pp. 6447– 6456

2017
[33]

Flow-guided one-shot talk- ing face generation with a high-resolution audio-visual dataset,

Z. Zhang, L. Li, Y . Ding, and C. Fan, “Flow-guided one-shot talk- ing face generation with a high-resolution audio-visual dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 3661–3670

2021
[34]

On estimation of a probability density function and mode,

E. Parzen, “On estimation of a probability density function and mode,” The Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1065–1076, 1962

1962
[35]

Adam: A method for stochastic optimization,

D. P. Kingma, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014

2014 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [7]

Faces that speak: Jointly syn- thesising talking face and speech from text,

Y . Jang, J.-H. Kim, J. Ahn, D. Kwak, H.-S. Yang, Y .-C. Ju, I.-H. Kim, B.-Y . Kim, and J. S. Chung, “Faces that speak: Jointly syn- thesising talking face and speech from text,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, 2024, pp. 8818–8828

work page 2024

[2] [20]

Generating talking face with controllable eye movements by disentangled blinking feature,

S. Liu and J. Hao, “Generating talking face with controllable eye movements by disentangled blinking feature,” IEEE Transactions on Visualization and Computer Graphics , vol. 29, no. 12, pp. 5050–5061, 2022

work page 2022

[3] [22]

A lip sync expert is all you need for speech to lip generation in the wild,

K. Prajwal, R. Mukhopadhyay, V . P. Namboodiri, and C. Jawahar, “A lip sync expert is all you need for speech to lip generation in the wild,” in Proceedings of the 28th ACM international conference on multimedia, 2020, pp. 484–492

2020

[4] [1]

Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

Introduction Talking Face Generation (TFG) aims to synthesize realistic fa- cial animations synchronized with speech, enabling applica- tions in virtual avatars, digital assistants, and media produc- tion. Recent advancements, particularly diffusion-based meth- ods, have significantly improved lip-sync accuracy and facial expressiveness, drawing increasin...

work page Pith review arXiv 2025

[5] [2]

Early works, such as Chen et al

Related works Talking face generation (TFG) has predominantly utilized audio-driven methods to create speaking portraits from a static image and audio input. Early works, such as Chen et al. [14] and Prajwal et al. [15] convert speech into mouth movements, laying the foundation for TFG. SadTalker [2] improved realism using implicit 3D coefficient modulati...

work page

[6] [3]

The overall pipeline is illustrated in Figure 2

Method We propose Face2V oiceSync, a scalable text-driven talking face generation method. The overall pipeline is illustrated in Figure 2. In Section 3.1, we provide an overview of the Face2V oiceSync framework. Section 3.2 details V oiceAdapter and its training strategies. Finally, Section 3.3 introduces DCTS, our proposed evaluation metric. 3.1. Face2Vo...

work page

[7] [4]

Experimental Settings We conduct our experiments using LRS2 [25] and HDTF [26] datasets

Experiments 4.1. Experimental Settings We conduct our experiments using LRS2 [25] and HDTF [26] datasets. The LRS2 dataset contains talking face videos paired with transcription labels. We split it into 3,347 speakers for training and 318 speakers for testing. HDTF consists of high- quality YouTube videos with more than 300 speakers, which we use for test...

work page

[8] [5]

Unlike prior works assuming a fixed face-to-voice mapping, we model it as a probability distribution problem, capturing natural voice variability

Conclusion We propose Face2V oiceSync, a scalable text-driven talking face generation method that synthesizes natural, identity-consistent animation and speech from a single image and text. Unlike prior works assuming a fixed face-to-voice mapping, we model it as a probability distribution problem, capturing natural voice variability. A lightweight V AE b...

work page