OmniTalker: One-shot Real-time Text-Driven Talking Audio-Video Generation With Multimodal Style Mimicking
Authors: Zhongjian Wang
Peng Zhang
Jinwei Qi
wang guang yuan
Sheng Xu
Bang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: talking head
audiovisual generation
text-driven talking head
Primary area: applications
TL;DR: We present OmniTalker, the first end-to-end framework for joint text-driven speech and talking head generation. It achieves 25 FPS synthesis while preserving speaker identity and synchronizing audiovisual outputs in one-shot settings.

Abstract: Although significant progress has been made in audio-driven talking head generation, text-driven methods remain underexplored. In this work, we present OmniTalker, a unified framework that jointly generates synchronized talking audio-video content from input text while emulating the target identity's speaking and facial movement styles, including speech characteristics, head motion, and facial dynamics. Our framework adopts a dual-branch diffusion transformer (DiT) architecture, with one branch dedicated to audio generation and the other to video synthesis.
At the shallow layers, cross-modal fusion modules are introduced to integrate information between the two modalities. In deeper layers, each modality is processed independently, with the generated audio decoded by a vocoder and the video rendered using a GAN-based high-quality visual renderer. Leveraging DiT’s in-context learning capability through a masked-infilling strategy, our model can simultaneously capture both audio and visual styles without requiring explicit style extraction modules.  Thanks to the efficiency of the DiT backbone and the optimized visual renderer, OmniTalker achieves real-time inference at 25 FPS.
To the best of our knowledge, OmniTalker is the first one-shot framework capable of jointly modeling speech and facial styles in real time. Extensive experiments demonstrate its superiority over existing methods in terms of generation quality, particularly in preserving style consistency and ensuring precise audio-video synchronization, all while maintaining efficient inference.

OmniTTalkr: One-shot Real-time Text-Driven Talking Audio- Audio-Video Generation With Multimodal Style Mimicking
Zhongjian Wang'
Peng Zhang'
Jinwei Qi Bang Zhang
Guangyuan Wang
Sheng Xu
Tongyi Lab, Alibaba Group
Project Page: https:/hhumanaige hit.ggaoooooooooiallaaaee.
Abstract
Although significant progress has been made in audio-driven talking head gen- eration, text-driven methods remain underexplored. In this work, we present OmniTalker, unified framework that jointly generates synchronized talking audio- video content from imput text while emulating the target identity's 's speaking and facial movement styles, including speech characteristics, head motion, and facial dynamics. Our framework adopts dual-branch diffusion transformer (DiT) ar- chitecture, with one branch dedicated to audio generation and the other to video synthesis. At the shallow layers, cross ross- fusion modules introduced integrate information between the two modalities. In deeper layers, each modality is processed independently, with the generated audio decoded by vocoder and the video rendered using a GAN-based high-quality visual renderer. Leveraging DiT's in-context learning capability through masked-infillinn simultaneously capture both audio and visual styles without requiring explicit style extraction modules. Thanks to the efficiency of the DiT backbone and the optimized visual renderer, Tanner achieves real-time inference at 25 FPS. To the best of our knowledge, OmniTalke is the first one-shot framework capable of jointly modeling speech and facial styles in real time. Extensive experiments demonstrate its superiority over existing methods terms of generation quality, particularly in preserving style consistency and ensuring precise audio-video synchronization, all while maintaining efficient inference.
strategy, model can
Introduction
Recent advancements in talking head generation (THG) have been predominantly driven by break- throughs in generative architectures. Although current research predominantly focuses on audio- driven THG 337 57
60), emerging applications in conversational AT and human- computer interaction increasingly demand text-driven solutions particularly given the paradigm shift enabled by large language models (LLMs). Despite this technological imperative, text-driven THG methodologies remain comparatively underdeveloped relative to their audio-driven counterparts.
45
62
16
typically employ a cascaded architecture combining text-to-speech (TTS) systems with audio-driven THG models. This conventional paradigm suffers from three fundamental limitations: (1) computational redundancy through duplicated feature processing, (2) error propagation across disjoint subsystems, and (3) andio-visual style discrepancies. Recent attempts to address these issues, such as UniFlug (35 that utilizes TTS latent features for facial keypoint generation, remain constrained by unidirectional andio-to-visua information flow.
Existing text-driven approaches 61 59
Equal Contributions
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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While methods like [4] demonstrate end-to-end generation capabilities, they require training or finetuning for every identity, severely limiting practical deployment..
The synthesis of expressive talking heads further necessitates precise modeling of multimodal speaking styles that simultaneously capture vocal characteristics, head motion patterns, and facial dynamics. Current solutions predominantly address style modeling in audio-driven contexts through either categorical emotion labels [46] 19 or video-based motion aggregation [33 (5).. However, these approaches fail to holistically represent the complex interplay between acoustic and visual style components that characterize natural human communication.
To overcome these challenges, we present OmniTalker, an end-to-end framework for one-shot text- driven talking head generation with unified audio-visuae style transfer. Our architecture integrates three key innovations: (1) A multimodal diffusion transformer (DiT) backbone enabling bidirectional cross-modal attention across audio and visual streams; (2) Dual transformer decoders for modality- specific refinement while preserving cross-contextual information; and (3) masked infilling strategy that leverages DiT's in-context learning capabilities for joint audio-visual style transfer without dedicated style extraction modules. The complete system is trained on a large-scale multimodal dataset.
Our principal contributions can be summarized as follows:
We propose OmniTalker, the first end-to-end unified framework that generates synchronized, high-quality audio-visual talking heads directly from text input in a one-shot learning paradigm.
OmniTalker represents the first comprehensive solution for fully replicating an individual's speaking style, including the dynamic triad of vocal characteristics, head motion patterns, and facial expression dynamics.
The proposed architecture achieves real-time inference single NVIDIA RTX 4090 GPU. It surpasses existing approaches in generation quality, with particular improvements in style preservation and audio-visual synchronization precision.
2
Related Work
We present the most relevant lines work here, but since talking head research is vast, detailed comparison with prior methods is included in the Appendix A
2.1 Audio-driven Talking Head Generation
Early audio-driven THG works 37 [6] primarily focuses on lip-synchronizzation by modifying
mouth regions in target videos based on audio input. Recent advancements have extended this
paradigm generate talking head videos from single reference images 445
33
14 57
Most
state-of-fe of-the-art methods adopt a two-stage framework: (1) mapping audio signals to intermediate
motion representations (e.g.. 3DMM coefficients [62 63), facial landmarks [64] 27 588, or learnable
latent codes [57] 23)). ). followed by (2) video synthesis conditioned on the predicted motion. While
these methods have achieved remarkable progress, their reliance on audio input constrains practical applications in scenarios where only textual input is available.
Our work inherits the two-stage generation paradigm for its effective decoupling of identity, head pose, and facial expressions. However, we propose novel end-to-end architecture that predicts head pose and facial expressions directly from textual input rather than audio signals.
2.2 Style-Controlled Talking Head Generation
Existing approaches typically adopt simplified formulations: some works [46] 19 41 represent
styles as discrete emotion categories, while others (28 22) employ reference videos for frame-level expression control an approach that fails to capture the temporal dynamics of facial expressions. StyleTalk (33 introduces a more sophisticated solution by extracting spatiotemporal style codes from reference videos, though this method primarily focuses on expression styles and neglects head pose variations.

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Current literature predominantly reduces speaking style to facial dynamics, overlooking two critical aspects: (1) the inherent correlation between vocal and facial styles, and (2) the semantic dependency between speaking style and linguistic content. This limitation motivates our work to develop more comprehensive style modeling framework.
2.3
Text-driven Talking Head Generation
Text-driven talking head generation systems aim to jointly synthesize speech audio and corresponding facial animations from textual input. The predominant approach employs cascaded pipeline combining a text-to-speech (TTS) module with an audio-driven talking head generator [61] 59 53
43
24 Cascaded systems inherently limited by three key issues: (1) computational redundancy through duplicated feature processing, (2) error propagation across disjoint subsystems, and (3) mismatches between audio and visual stylistic characteristics.
35
Recent efforts have explored parallel generation architectures to address these issues 9 35
21 3]
Among existing solutions, TTSF [21]. NEUTART, and AV-Flow (3] share similarities with approach in employing cross-mos modal conditioning. However, TTSF predicts sound directly from input images, which, while reducing dependency, fails to replicate the specified identity. Both AV-Flow and NEUTART are person-speciie methods, requiring training for individual subjects.
Method
We aim perform joint audio-visual generation using compact neural architecture that ensures alignment between audio and visual modalities while preserving the speaking style (both vocal and facial) from reference video. Inspired by the in-context reference commonly used in LLM and TTS[4] as well as the dual-sine single stream hybrid diffusion transformer(Di)) structure utilized in text-to-image synthesis 13 25), propose the network architecture illustrated in Figure []] The architecture integrates three core components: (1) Multi-Modal feature extractors to capture reference dynamics, (2) dual-branch DIT network for parallel audio-visual synthesis, and (3) an Audio Visial fusion module ensuring tight synchronization. Unlike previous approaches, we aim at the domain of multimodal generation.
sodio
noekeed gounstte)
Audio
Cross-Attcation
(b) Add Pasion
Audio
Video
Istent
Video latent
motion extnect
reference
Mii cxtrac
oal ateeraa
masked
projucter
spozch projator A
Audio Intent
Video
Audoo latent
Vileo latent
x²
moise
noise
(c) Linear-F undon
(d) MM-attcation Fusion
Andio latens
Video
Audio lasest
Video likent
DiT Black with Audio-Vilas Freion
Project
Inear hejert
Lieae Project
setales
vucoder
Audio
Video
Audio latent
Video lntent
Mei Spectrogmam
Hial Mation & Facial Dymmii
1- -edtaee -a ---000000
C
Cancal
+
Add
Informnce Time Only
Masket
Discard
Frooen
Trainbbee
geserated audio-video
Figure 1: The architecture of OmniTalker. A dual-branch DIT framework with modal fusion for joint audio-video generation from text(left). Four variants of Audio Video fusion modules are explored(right)
3

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3.1 Multi-Modal Feature Alignment
Our model takes two inputs: the driven text Ta and a style reference video, to produce a realistic talking face video. We first separate the audio and video streams from the reference video, denoting them as A, and V,, respectively. ASR models are adopted to convert A, into transcript text T, as reference text.
Audio Feature A mel-sppetrogram M, E FxN. is extracted from A,, where F the mel dimension and N₂ is the length of the reference sequence(at 94 fps by default). A MLP--baed module integrates the spesttorto, to obtain the audio embeddings x².
Visual Feature We extract visual codes C, E R61XNN for each video clip, which consists of facial expression blendshapes Chepp E IR head pose [R: 1] E R and eye movement coefficients deye R for each frame. Blendshape is adopted for its more explicit semantic interpretations compared to the commonly used 3DMM and Veree 49] coefficients. We firstly detect facial landmarks V28 from each frame and optimizes C. = [Ceap: Oleye R. t) by minimizing distances between the projected keypoints and the detected landmarks as follows:
S + AespOeap + Aeye - V2all
Limk = P. x T
where P, is the orthographic projection matrix, T E RR3x4 is the similarity transformation matrix constructed by [R; t). S is the mean 3D shape, Aexp is the expression base and Aezp is the eye movement base. N, denotes the number of frames in the video sequence, which is 30 fps by default. To align N,, with N.. we upsample C, to 94 fps through interpolation. C, is then projected to the visual embeddings x" by a MLP-based embedding module.
Input Text Both the driving text Ta and the reference text T, are converted into a pinyin sequence (for languages) [4]. The two character sequences
Chinese) or character/letter sequences (for are concatenated and padded with filler tokens o of the same length as x". Let ST represent the final sequence. The text embedding module is built upon ConvNeXt-V2[55. which is common approach in the field of TTS, due to its strong temporal modeling capability. The projected text embeddings, denoted e serves as condition for both audio and visual branches.
3.2 Multi-Modal Feature Fusion
Figure | illlustrates our network, which consists of multiple DiT blocks designed to handle both audio and visual data streams. The network is structured to facilitate the cross-modal fusion between audio and visual features, enabling the generation of coherent and synchronized outputs.
Text Conditioning As detailed in Section 3.1 the model architecture processes three modalities: text (as character sequences), audio, and visual features. For the text modality, we apply an absolute sinusoidal position embedding before feeding it into the ConvNeXt blocks. This design allows the text representation develop moderate level of modality-specine
modeling capacity through the ConvNeXt blocks, enabling better alignment with other modalities before cross-modal fusion and in-context learning with other inputs. Diverging from MMAudio's 's 6 approach that maintains a separate text branch, our architecture directly concatenates text embeddings with the audio and visual modalities. This decision is motivated by our observation that the pure MMDiT structure may be overly flexible for tasks requiring high-fidelity generation aligned with explicit prompt guidance, e.g. TTS and THG. To preserve spatial coherence in the multimodal input, we augment the concatenated sequences with convolutional position embeddings.
This module is responsible for the integration of audio and visual features. It employs dual-branci architecture, where one branch processes visual information, and the other processes audio. As for feature fusion mechanism, common strategies include (a) Cross- Attention, (b) Element-wiss A Addition, (c) Linear Fusion (3). (d) Multi-modal joint attention(MM.
Audio-Visual Fusion Module:
Attention) [3 25 6), as shown in Figure [ After empirical validation, we adopted MM-Attention which demonstrated superior performance. We will provide detailed comparison of the effects of different fusion methods in Section 4.4 MM-Attention, the Query(Q), Key(K), and Value(V) matrices are derived from both modalities in joint attention, and RoPE [44 is applied. MM-Attention allows the network to dynamically weight the importance of audio and visual features, ensuring that the generated video is temporally aligned with the input audio.

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Single-Modality DiT Blocks: Following the Audio Visual Fusion Module, the network employs several single-modality DiT blocks. These blocks operate on the fused multimodal features but are designed to refine the generation process by focusing on individual modalities (audio or visual) after the initial cross-modal fusion. This two-stage approach, first fusing multimodal information and then refining each modality separately, allows the network to maintain the coherence of the generated content while ensuring high-quality output for each modality.
3.3 In- In-Context Stylized Audio-Visual Generation
We propose an audio-visual sequence infilling framework that predicts target segments by leverag- ing contextual information from surrounding segments and full text transcriptions (including both reference text and driving text). During training, we implement a masked reconstruction strategy where multiple random segments in the audio-visual sequence are occluded. The model is optimized through audio-visual reconstruction loss computed on these masked regions. This in-context ref- erence mechanism enables the network to implicitly learn stylistic features from reference videos without requiring complex additional style extraction modules. For inference, our approach employs three- compoeentt input structure: (1) a reference audio-visual pair serving as the talking style prompt, (2) arbitrary driving text as the conditional input, and (3) a noisy latent placeholder representing the target audio-visil visual sequence to predicted. This architecture allows the model to synthesize audio-visual synchronized sequences that inherit the stylistic characteristics defined in the reference prompt while maintaining alignment with the provided textual condition, as illustrated Figure] []
Audio-Visien Generation via Flow Matching We employ flow matching (30] to train our DiT- based model, leveraging its superior advantages such as improved training and inference efficiency, simplified optimization, and faster convergence compared to traditional methods. Let x E IR donate the data points(mel-spectrogree
M, and visual codes C, in our case), sampled from the ground truth data distribution ((x), and p [o, 1] x R + IR > O donate the probability density path, which is a time dependent probability density function. Our model with parameters @ predict a vector field vy, which can be used to construct time-dependent flow, Φ [0, 1] Rd - IR via the ordinary differential equation (ODE). Follow the Optimal Transport (OT) formulation from (0), we can train our model with conditions C [Mr. Cr, Sr] by optimizing the conditional flow matching objective:
LCFM (0) = (..(())))(( [10:((1 - t) + 1æ C: - (x1 - =0))
(2)
We provide detailed preliminaries of flow matching in Appendix B. In the training stage, we pass in the model the noisy audio-visual embeddings
t) tx and the masked embeddings
(1 ()) @ 31, where x1 = [M,; Cr.. To denotes sampled Gaussian noise and m E {0, 1) (F+
represents binary temporal mask. The model is trained to reconstruct @ 2 with (1 m) O 2
and Sr. We apply the conditional flow matching loss function formulated in Equation (2) for both the audio and visual branches. The overall objective is:
L = Cm + A,.COM + -yFCCrM + N.CCFM
(3)
where m is for mel-spectrogram, h is for head pose, f is for facial expressions and e is for eye movements correspondingly. We use loss instead of 1.2 as we found that an I.I loss leads to more realistic results. During inference, we could push the data from the Gaussian distribution to the target distribution by any ODE solver.
Classifier- Free Guidance To further improve generation quality and enhance style replication, we integrate classifier-fre free guidance (CFG) into both branches. We randomly drop each of the input conditions C = [M, Cr, Syl in the training stage, while during inference, we apply multi-conditions CFG to construct the in eCFG:
oy CFG
ad) (x(
Σa ve 0)
cEC
GEC
where Or is the guidance strength for condition c. Please refer to Section 4.1 for the detailed parameter settings mentioned above.
3.4 Audio And Video Decoders
The synthesized mel-spectrogram decoded to speech via Vocos 42 a high-fidelity vocoder selected for its real-time efficiency. Alternative vocoders (e.g., Big can be flexibly integrated.
5

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We design a warp base- GAN model as the visual render(similar to [57 II, which randomly selects frame from the reference video as the identity reference and generates video frames based on the visual codes C₁ (including head pose, blendshapes and eye movements) predicted by the DiT network. produces 512 x 512 resolution frames at fps, ensuring real-time performance. While the visual rendering pipeline is beyond the scope of this work, it is designed to be both computationally efficient and highly flexible. We refer readers to the Appendix B.2 for more details.
4 Experiments
4.1 Experimental Setup
Implementation Details. Our model includes 22 audio-visual fusion blocks with two parallel branches (4 single-modality DiT blocks each), 5 512-dim embeddings (audio/visual via linear layers, text via 4 ConvNeXt V2 blocks), totaling 0.8B parameters. The model was trained on 8 NVIDIA A100 GPUs using batch size of 12,800 frames per GPU for 750,000 iterations with learning rate of le - 4. In Equation 33. we use Am = 0.1, 2 = 3.0, An, = 0.5 and Xe = 0.5. The audio features 100-D(F= 100) log-ma mmel filterbank coefficients extracted with 24kHz sampling rate and hop length 256, yielding an audio sequence with approximately 94 fps. In contrast, visual codes captured 30 fps and upsampled 94 fps to match the audio frame rate. During inference, the duration of reference audio-visual content is restricted to 1-10 seconds, with any excess truncated. The CFG parameters set to O.M, 2.0, ac,, 2.5 and 055 2.0, while 16 sampling steps used. We provide full details in Appendix
represented
Datasets. We evaluated method both audio and video generation tasks. For audio, employed the SEED[]] dataset, while for video generation, 500 clips were randomly selected from VoxCeleb2 the test set. To further validate the robustness of our approach in practical applications, 100 video clips from Chinese real-world scenarios were selected as Custom dataset for supplementary testing of audio and video quality. We pre-trained our model on collection of large- scale open-source talking-head datasets and subsequently developed a high-quality dataset(totaling 690 hours) to fine-tune the model's performance. We direct the authors Appendix C.T for more details.
Compared Baselines. Since there is currently method capable of jointly generating audio and video simultancously, we conducted comparative analyses on text-to-speech (TTS) methods and audio-driven talking head approaches separately. For the TTS module comparison, we selected three representative methods: MaskGCT(54), FSTTS[4], and Cosy Vosce Since SEED lacks visual information, we ac, 0.0(w/o visual condition) during testing. On the Custom dataset, we separately evaluated the scenarios ac, = 0.0 and ac, = 2.5(w. visual condition). In the evaluation of Talking Head generation modules, established multimodal comparative framework
Head Pose Consistency
0.5
0.4
0.3
Sad Takker
Aui Talker
StyleTalk
EichaMimii
0.1
Hallo
Reference
300
frannes
Figure 2: We visualize the values of head poses over time for the reference video and videos generated by different methods. To better showcase the differences, we focus on displaying the Yaw angle. The red dashed line separates the values of the reference video on the left from those of the generated results on the right. StyleTalk ** can only mimic the expression style and simply copy the reference head pose.

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Style Referenco
SyleTalle+
SodThiker
AniTakee
EchoMinic
Hallo
Figure 3: Qualitative Comparision. Given a video for identity and style reference, we visualize generated frames of all compared methods. We also visualize the motion heatmaps of generated videos on the right side. For the audio-driven approach, we use the audio output from our own method the input. StyleTalk** ** directly transfers the reference pose sequence.
encompassing: (1) two GAN[15] based methods (SadTalkerll 62] and AniTalker 331); (2) two state-of- the-art diffusion model-based approaches (EchoMimic [5] and Hallo(56)): and (3) StyleTakk(33] an audio-driven THG method with dyle-preservatie capabilities. It is important to note that all talking head models in our experiments employed audio signals generated by our proposed method as the driving input, ensuring fairness and comparability across evaluations. All methods were evaluated 512 512 with single NVIDIA RTX 4090 GPU, whereas Sadtalker and AniTalker employ super-resolution model by default.
Evaluation Metrics. We evaluate audio quality using the word error (WER), measured with the Whisper-Large-v33 38 model. Additionally, we employ speaker similarity (SIM-A) to assess the performance of zero-shot timbre preservation. The evaluation metrics utilized in the portrait image animation approach include Fréchet Video Distance (FVD) (48). Synchronization-CC (Sync- C) 40 Specifically, FVD measures the similarity between generated videos and real data, with lower values indicating better performance, and thus more realistic outputs. Sync-C evaluates the lip synchronization of generated videos in terms content and dynamics, with higher Sync-C scores denoting better aligmment with audio. We also compute the cosine similarity(CSIM)) between facial identity features of the reference image and generated frames. Following EMO[47 the preservation of speaking style by calculating the Fréchet distance between motion coefficients extracted from the reference and generated videos. separately evaluate expression and pose fidelity, we introduce E-FID (Expression FID) and P-FID (Pose FID). Specifically, F-FID integrates facial blendshapes Geap and eye movements Oryer while P-FID quantifies head pose consistency through [R; t] parameters.
(6), we assess
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4.2
Quantitative Evaluation
Table Quantitative comparison with TTS methods.
SEED
Custom
Method
ZH
EN
ZH
WER(%)
SIM-A
WER(%)
SIM-At
WER(%))
SIM-At
Cosy Voice 12
3.10
0.723
4.29
0.609
3.52
0.725
MaskGCT 54
2.27
0.774
2.62
0.714
2.83
0.769
F5-TTS(4]
1.56
0.741
1.83
0.647
1.82
0.762
Ours(ac,
1.55
0.766
1.85
0.685
1.61
0.771
Ours(ac, = 2.5)
1.58
0.762
Table 2: Quantitative comparison with SOTA audio-driven talking head generation methods. We
highlight the best, second best.
VoxCeleb2 Sync-C1
Cisstom Sync-C1
Inference
Method
FVDL
CSIMT
B-FID
P-FID]
FVDL
CSIMT
B-FID
P-FID
HPST
SalTalker 6.
243.92
0.828
5.34
0.67
3.37
595.00
0.854
3.61
0.45
9.23
25
AniTalker
0.842
329.70
0.858
StyleTalk 331
144.40
0.691
5.52
0.71
0.31
208.51
0.791
4.17
0.51
0.40
21
EchoMimic
191.77
0.883
5.88
0.60
2.74
367.74
0.908
4.74
0.36
8.26
0.78
Hallo|50 [56]
156.33
0.879
6.53
0.59
1,03
244.40
0.887
5.52
0.35
1,06
0.65
Ours
102.54
0.890
6.37
0.02
0,04
176.32
0.905
5.48
0,08
0.12
25
our approach exhibits significant superiority over TTS baseline models. WER is notably reduced on both SEED- ZH and Custom test sets, while showing only marginal underperformance compared to F5TTS on SEED-EN, demonstrating that the generated audio strictly adheres to the input text. Additionally, our method ranks second SIM-A metries, further validating the preservation capability of identity features under zero-shot conditions. Notably, in the Castom dataset, incorporating visual conditions achieves lower WER values, indicating that introducing visual supervision effectively enhances perceptual audio quality. We attribute this improvement to the effectiveness of multi-task learning, as audio signals and facial motion patterns represent highly correlated modalitics.
Audio Results. In quantitative analysis of audio generation as demonstrated in Table
Video Results. Table 2 further demonstrates the superior performance of our method in visual generation. On both the VoxCeleb2 and Custom datasets, our approach achieves SOTA performance on 5 core metries, with the lowest FVD and highest CSIM scores. These metrics confirm our model's significant advantage in generating high-quality videos while preserving identity consistency. Notably, our method achieves orders-of-magnee magnniuue improvements on the E-FID and P-FID metrics compared to existing approaches, highlighting its exceptional capability preserving facial motion patterns and head pose characteristics. This enables effective inheritance of the reference person's 's speaking style, leading to high-fi high-fidelity expression cloning with remarkable authenticity. While our method obtains suboptimal results the Sync-C, slightly underperforming the diffusion-basse method, empirical analysis reveals that these metrics favor front-facing video generation. In contrast, baseline methods tend to produce front-facing videos at the expense of directional information from the reference images. Comparison videos are provided in the supplementary materials for further insight.
User Study. We performed MOS evaluation (1-5) based on (60 with 50 volunteers to assess perceptual quality. Participants rated videos on six aspects: speech similarity, rhythm, visual quality, speaking style, lip-syne, and pose-sync, details in Appendix C.4 The results are shown in Table 3 Our method achieved the best performance in speech similarity. visual quality, speaking style, and pose synchronization, with significant improvements in style preservation and pose generation over competing methods, highlighting our state-of-the -fthe-r capabilities in maintaining natural speaking styles and generating synchronized facial movements.
Inference Efficiency, Our method achieves high-quality output with real-time inference capabilities through the innovative integration of flow matching technology and a relatively compact model architecture (with only 0.8 billion parameters). As shown Table 2 our method outperforms others in both inference speed and output quality. It is noteworthy that the fps number of baseline methods do not incorporate the inference time of the preceding TTS process. other words, none of these methods can truly achieve real-time performance in practice.

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Table 3: MOS score of different methods by user study.
Cosy Voice
MaskGCT
FSTTS SadTalker AniTalker StyleTalk
EchoMimic
Hallo
Ours
Speech Similarity
3.56
4.32
4.40
4.66
Speech Rhythm
4.24
3.88
3,90
4.02
Visual Quality
3.86
3.90
3.32
3.96
4.00
4.12
Speak Style
2.10
2.56
3.98
3.88
3.94
4.57
Lip-syne
4.00
3.90
4.00
3.82
4.30
4.20
Pose-syne
1.22
1.60
1.98
3.00
3.52
4.44
Table Ablation study of Fusion Strategies.
Fusion
WER(%) (
SIM-At
Sync-CT
E-FID.
P-FIDD
Add
2.89
0.582
4.92
0.58
3.20
Linear
2.32
0.645
5.08
0.21
1.54
Cross-Attention
1.72
0.698
5.35
0.12
0.18
MM-Attention
1.58
0.762
5.48
0.08
0.12
4.3 Qualitative Evaluation
Facial Motion Styles. To more intuitively demonstrate the superior capability of our method in modeling facial motion styles, we compared the cumulative heatmaps of head movements in speaking videos generated by our method and other existing approaches. As shown in Figure 3 by comparing the generated results with reference videos, it is evident that the heatmaps produced by our method align closely with those of the real data. This result indicates that method possesses significant advantage in capturing and reproducing complex facial motion styles. Figure further validates this conclusion from temporal perspective. We selected the yaw angle parameter of head pose) as the tracking metric. The red line on the left represents the reference sequence, while the right side displays the sequences generated from various methods. The results show that the generated sequences of our method maintain high consistency with the reference sequence in terms movement while preserving necessary differences, demonstrating
of amplitude and frequency effective inheritance of the head-post style. In contrast, the head movements generated by other methods were generally less pronounced and lacked dynamic variation. should be noted that the StyleTalk method directly transfers the reference pose sequence to achieve generation, which ensures complete consistency with the reference pose but fails to consider the semantic association between speech content and pose.
4.4 Ablation Study
Fusion Strategies. As shown in Table the MM-Attention method achieved the best performance all terms. This suggests that employing MM-Attention as the fusion strategy for audio and motion features may be the most effective approach. It better captures the correlations and importance distributions between the two modalities, thereby enhancing the overall performance. Although others can also achieve feature fusion, their performance generally lags behind that of MM-Altention. In particular, the Add method performed the worst in evaluation metrics. This may because simply adding or linearly combining the two features fails to adequately account for their complex interactions and the nuanced importance distributions inherent to each modality.
5 Conclusion
OmniTalker introduces a unified framework for text-driven talking head generation, achieving simultaneous audio-visual synthesis with in-context style replication. By integrating dual-branch architecture with cross- modal attention mechanisms and in in-contex style reference, the method bridges the gap between text input and multimodal output, ensuring audio-visual synchronization and stylistic consistency without reliance on cascaded pipelines. The combination of a lightweight network architecture and flow matching based training enables model to deliver high-quality output with real-time inference capabilities. Extensive experiments demonstrate that our method surpasses existing approaches generation quality, particularly excelling in style preservation and audio-video synchronization, while maintaining real-time prediction efficiency.

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A
Comparison with Prior Methods
Table 5: Capabilities of different methods
Method
text driven
one-shot
voice clone
style replicate
real-time
Wav2Lip
VividTalk
45
SadTaller
StyleTalk 33
VASA-1
EchoMimic
Hallo[56]
T1
V
AV-Flow 3
Ours
V
B
Model Details
Our model includes 22 audio-visual fusion blocks with two parallel branches (4 single- modality DiT blocks each), 512-dim embeddings (audio/visual via linear layers, text via ConvNeXt V2 blocks), totaling 0.8B parameters. We provide detailed configurations in Table
Table 6: Model Configuration
Module
Hyper Parameter
Value
ConvNeXt-V V2 blocks
TextEmbedding
Embedding dimension
512
FFN dimension
1024
Linear Layer
AudioEmbedding
Embedding dimension
1024
Linear Layer
VisualEmbedding
Embedding dimension
1024
Transformer blocks
22
Attention heads
16
Audio-visual Fusion
Embedding dimension
1024
FFN dimension
2048
Transformer blocks
AdaLayerNorm
Linear Layer
Audio DIT branch
Attention heads
16
Embedding dimension
1024
FFN dimension
2048
Transformer blocks
AdaLayerNorm
Linear Layer
Visual DiT branch
Attention heads
16
Embedding dimension
1024
FFN dimension
2048
B.1 Preliminaries on Flow Matching
Flow matching, evolved from Continuous Normalizing Flows (CNFs) 30 aims learn model that transforms a simple distribution Po into a more complicated one This objective aligns closely with the fundamental goal of diffusion models. The learning process is achieved by minimizing the difference between the flow of the data and the flow predicted by the model, with simple objective:
LFM(0) (xp)(() [1v((x) (x)
(5)
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where x' denotes data points, (z) represents time dependent probability density path, and (z(2)
das denotes the unknown time-dependent vector field governing the trajectory of the data distribution de from from Po to P1-
Specifically, given a specific sample 21 from some unknown data distribution g(z1). Px (x(2(21) refers the conditional probability path. The marginal probability path can be obtained by taking the expectation of the conditional probability path over all samples in the data distribution:
(z(x) =
= (P(()))))(
(6)
Assuming Pa(alay (x1) is derived from the conditional vector field (x(xx)), it follows that ux(x) and (x(2))) satisfy the following relationship:
( dz1 (x(e))))))(((
uo(()aa (((xl(11)))))){)))) (1(ola1)g(11))
ue (2)
(r(x)
Therefore, the original LFM can be reformulated as:
LCFM (0) Et (((())))((((()))) [[u,((;; (x(x(xx))
(8)
We consider the case of Gaussian distributions and adopt a simple yet effective approach: the optimal transport (OT) path. Given the target data point 11 and the current data point xı, the most efficient way to go with a straight line. The conditional vector field is then defined as (x(l)11) = (I1 - 21 (1- In this case, the CFM loss takes the form:
CCFM (0) (.. (x(())) 1|0z ((1 - ()ar + t01; 0) - (x1 =z))
(9)
as described in Section
B.2 Render
Source Image
Appearance feature extractor
3D Face Recon Proj
Explicit keypoint
Warping module
Generator
3D Face Recon Proj
Explicit keypoint
Driven Image
Figure 4: Overview of visual renderer.
To balance the quality of generated videos and model inference performance, we employ a warping- based GAN framework inspired by existing works [8). The general framework consists of four key components as shown in Figure 4 1) Appearance feature extraction to get visual features from source image; 2) Motion representation extraction that extracts explicit facial keypoint to represent face movement; 3) Warping field estimation to calculate the transformation from source to target; and 4) Generator to synthesize final images from warped appearance features. Specifically, face motion is represented by 3D keypoints projected from a 3DMM head template 44),, and 3D keypoints on key face regions are projected from the head template, including eyes, mouth, eyebrows and face contours, driven by 3DMM coefficients, which are capable of representing a diverse range of facial expressions.
We follow LivePortrait 18 using perceptual loss Lrer, GAN loss LGAN, reconstruction loss L for render network training. To enhance the quality of mouth region, we obtain the mouth mask and
15

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introduce mouth region perceptual loss Mouth, The overall training objective is formulated as follows:
Per
C Cer + LGAN + Recon + Per
Mouth
(10)
During inference, the renderer generates the final video by projecting the visual codes predicted by OmniTalker onto 3D keypoints through 3DMM.
C Experiment Details
C.1 Datasets
Video
Detett
FaceDDetect
FareiTrcking
Stook I
VideoCrop
(b) Duration
(c) Char Count
hachslencshapp
Facelandmarr
SpeakerDatect
SpecchRooognize
Stage f7
FaceFitingg
Sync
Stage
2DMM
video
audio
text
(a) Pipeline of Data Preprocessing
(d) DNSMOS
(e) Face Size
Figure 5: Overview of our proposed pipeline and dataset.
We employ TalkingHead-IKH 52 VoxCeleb 36 and CelebV-HQ 65 as pre-training datasets, while constructing high-quality custom dataset for fine-tuning. This study proposes automated preprocessing pipeline for large-scale multimodal data curation, as illustrated in Figure [a The pipeline comprises three sequential stages, containing eight specialized processing modules strategi- cally designed to enable parallel execution and optimize computational efficiency through modular architecture.
Stage 1: Coarse Segmentation
Scene Detection:
PySceneDetect 110 with adaptive threshold (o 1.5)
Face Detection & tracking: Insightface [17 with IoU continuity > 0.5
Stage 2: Multimodal Feature Extraction
Facial motion:
Varee 44
(52 blendshapes + 6DOF pose + eye gaze)
Speaker Detection: LightASD
29
(confidence > 0.8)
Speech Recognition: V3-Lare
38
Stage 3: Fine Segmentation
Temporal constraints: Clip duration: 1s < t S. 30s & Phoneme rate: < 1s/character
Spatial constraints: Face bounding box > 15% frame area
Others: Audio quality: DNSMOS P.835 OVRL 39 (cutoff >> 2.5)
Figures 5b to 5e shows the distributions across duration. text length, quality score, and average face size custom dataset. It comprises approximately 300,000 clips, totaling 690 hours of high-quality multimodal data with synchronized text, audio, and video components.
C.2 Training
Strategy. We begin by performing large-scale pre-training on open-source multimodal datasets described in Appendix C.I to establish foundational capabilities in text comprehension and
16

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multimodal generation. During the training process, we implement a masking strategy that randomly masks 70-100% of the audio-visual sequences, compelling the model to learn sequence reconstruction abilities. Concurrently applying random dropout with a 0.2 probability across text, audio, and video conditioning inputs facilitates classifier-free guidance (CFG) training. In the final stage, we conduct fine-tuning on custom dataset, which significantly enhances the model's performance.
Sampling. We modified the timestep sampling distribution from a uniform distribution logit- norm distribution (mean=0.0, std=1.0) according to [13], and observed that employing logit-norm bias prioritize the selection of training timesteps significantly enhances model performance than uniform distributions.
uniform
nogitanormal
150
125
nm
100
75
50
0.0
0.2
0.4
0.6
0.8
Figure 6: logit-norm and uniform distributions that bias the sampling of training timesteps.
C.3 More Qualitative Evaluation
Eye Movements. Figures 9 and 10 demonstrate the enhanced performance of our proposed method in generating eye movements. As indicated by the highlighted red regions in the visualizations, our approach produces complex gaze trajectories with dynamic variations that exhibit significant temporal correlations with head pose changes. By contrast, the baseline method maintains fixed gaze direction determined at initialization, resulting in nearly static eye movements. This coordinated gaze-head
Monvement Consistency
Sappa ham
my..
-02
11.6
Auti auiialker
115
StyleTilk
Tallo
-12
300
500
Figure 7: Visualization of eye movements over time for the reference video and videos generated by different methods. The red dashed line separates the values of the reference video on the left from those of the generated results on the right.
17

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motion mechanism plays critical role in enhancing the biological plausibility of generated videos. Notably, Figure
Figure[fuutherr further validates our method's capability inherit eye movement characteristics from reference videos. Experimental results show that the generated gaze trajectories demonstrate heritable features in both frequency and amplitude parameters compared to reference videos, while maintaining necessary difference to avoid mechanical repetition while preserving consistency.
Emotional Generation. As illustrated in Figure g we employ reference videos from the RAVDESS 32 emotional dataset. Leveraging in-context learning capabilities, our methods en- ables to generate results highly aligned with target emotions. Experimental results demonstrate that the proposed method accurately captures expressive facial exprroexppesssios. This capability of generating multimodal emotional representations effectively validates the model's capacity for analyzing and reconstructing complex emotional states.
Reference Clips
Generate Clips
Calm
Happy
Sad
Angry
Disgust
Surprised
Figure 8: Visualization of Generation via different emotional reference.
C.4 User Study Settings
We conducted user studies for both audio and video generation following our quantitative evaluation. We selected 25 reference videos each from the VoxCeleb2 and Custom datasets, totaling 50 references. For the audio evaluation, we generated 50 audio samples per TTS method using the provided text, then employed our synthesized audios to generate corresponding videos for each THG method. Each participant was asked to evaluate the results across six dimensions: for audio outputs, speech (assesssii vocal timbre resemblance) and speech rhythm(evaluating prosody alignment with text punctuation): for video outputs, visual quality(focusin on image clarity, artifacts, and geometric distortion), speaking style(comparing emotional expression, head motion, and gaze patterns), lip- semantic accuracy of head gestures like nodding for affirmation).
sync, and seseessnccveerffyye Figure [T displays the interface used in our user study. 50 participants rated each generation result on 5-point scale (1-5), with average scores presented in Table 3. This comprehensive evaluation framework ensures multi-dimensional assessment of both audio-visual quality and semantic fidelity in human-like generation tasks.
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C.5
detail fidelity and More Ablation Study
We have conducted additional experiments validate the efficacy of the Dual-Branch DiT architecture, and the preliminary results align with our original conclusions.
Table 7: Modality ablation experiments
Setting
WERI
SIM-A1
Syne-C1
E-FID
P-FID.
text only
(a., 0.0,ac, = 0.0,as, = 2.0)
1.65
0.249
4.66
0.87
4.21
w/o audio
(aM, = 0.0,ac. = 2.5,055 = 2.0)
1.65
0.264
4.45
0.12
1.67
w/o video
(aM. 2.0,ac. = 0.0,asT = 2.0)
1.61
0.771
4.93
0.32
1.88
full
(OM, 2.0,ac, = 2.5,as, 2.0)
1.58
0.762
5.48
0.08
0.12
Table compares the performance of four different settings. The full model (WER=1.58, Sync- C=5.48) outperforms all ablated variants, demonstrating that cross-modal integration of audio and video enhances text-to-speech generation accuracy and improves temporal alignment between modal- ities. When reference audio is not provided, the model cannot replicate the original speaker's 's vocal characteristics, as evidenced by significant drop in SIM-A. However, the generated lip move- ments and head motions align naturally with the input text. Without reference video input, lip movements and head motions are primarily driven by audio loudness and textual content. Although synchronization remains coherent, person- -eessfn-peccficc details (e.g., unique facial expressions) preserved, resulting in significantly lower E-FID and P-FID scores. When only text is provided (without reference audio video), the model stochastically generates plausible speaking style, encompassing vocal tone, rhythm, emotional expression, lip motion, and head motion patterns. While speech articulation remains clear, the specific identity and style vary randomly. This highlights the effectiveness of the dual-branch architecture in enabling robust cross-modal learning and replication capabilities.
not
D Limitations and Future Work
Here we discuss the limitations of this work and potential directions for future improvement. Firstly, the scope of our method focuses primarily on dynamic generation of the head region without incorporating hand pose modeling or full-body motion control. This constraint limits the completeness and interactivity of generated content to some extent. The second challenge relates to generation quality: GAN-based approaches still face technical barriers when handling large-scale dynamic transformations. Notably, when motion amplitude exceeds critical thresholds, artifacts such as texture blurring and boundary discontinuities frequently emerge, compromising the visual realism of generated results.
To address these limitations, we propose optimizations along two dimensions. First, in the motion control dimension, we can implement a coordinated generation framework by integrating hand skeletal tracking data with full-body pose estimation information. Second, for generation quality enhancement, we recommend adopting video diffusion model-based generation paradigm. Compared traditional GAN architectures, video diffusion models demonstrate superior spatiotemporal continuity through their progressive denoising process. The progressive sampling mechanism effectively mitigates quality degradation caused by large-scale movements. Furthermore, combining attention mechanisms with spatiotemporal feature fusion strategies promises significantly enhance both dynamic smoothness in generated outputs.
E
Broader Impacts
Our work focuses on the efficient generation of realistic and expressive digital human videos, aiming to drive technological advancement with positive societal impact. By leveraging automation, real-time interaction, and multimodal perception capabilities, this technology enhances productivity across industries and accelerates innovation in the integration of text, audio, and video processing systems.
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It also fosters the emergence of new industries such as virtual idols and digital avatars, while enabling cross-lingual content generation that promotes cultural exchange across regions. However, the same technology carries risks: hyper-realistic deepfake algorithms could be exploited for disinformation campaigns, identity fraud, or synthetic media manipulation; sensitive biometric data collection required for digital humans may lead to large-scale privacy breaches if not properly secured; and prolonged engagement with virtual companions or personalized avatars might inadvertently create emotional dependencies psychological impacts. To address these challenges,
We incorporate visible watermarks into generated videos to proactively alert users that the content is synthetic in nature.
We embed imperceptible digital watermarks within both video and audio streams to enable traceability and track the origin of generated content, thereby requiring creators to consider potential legal and ethical risks associated with synthetic media production.
We are committed to advancing our methodology to enhance deepfake detection techniques, aiming improve the accuracy and reliability of automated detection systems through continuous algorithmic refinement.
Ours
SwdTaller
AnTTakeer
Style
EcheMimic
Hallo
Figure 9: Comparative Analysis of Eye Movement Visualization on IDI.
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AniTalker
TtykTilk
EchaMimic
Hallo
Figure 10: Comparative Analysis of Eye Movement Visualization on ID2.
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Study OmniTalker
Gankery
Spein Combery
Woled Luadit
Kpnallgge
Age
Spaak
Figure 11: The interface of user study.
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Our principal contributions can be summarized as follows: (1)We propose
OmniTalker, the first end-to-end unified framework that generates synchronized, high-quality
audio-visual talking heads directly from text input in zero-shot learning paradigm. (2) OmniTalker represents the first comprehensive solution for fully replicating an individual's speaking style, including the dynamic triad of vocal characteristics, head motion patterns, and facial expression dynamics. (3) The proposed architecture achieves remarkable compu- tational efficiency, operating 25 frames per second on single NVIDIA RTX 4090 GPU, thereby enabling real-time applications. Extensive experiments demonstrate that our method surpasses existing approaches in generation quality, with particular improvements in style consistency preservation and audio-visual synchronization precision.
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We have discussed the limitation of out approach in Appendix, Limitations
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Reviews:
[{"review_id": "hNjwcQekmM", "reviewer": "Reviewer_YtbX", "summary": "The paper proposes a unified framework for generating high-quality audio-visual talking heads from text input.", "questions": "1、 The authors should evaluate the core technical contributions through an ablation study\n\n2、Please clarify the technical contributions.\n\n3、Please clarify the symbols in Eqn. 3 and Eqn.4.\n\n4、How does Figure 2 validate the proposed method's performance in reproducing complex facial motion styles through the visualization of jaw angle dynamics?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe method enables real-time talking head generation on a single NVIDIA RTX 4090 GPU.\n\nWeaknesses:\n\n1、There are many writing issues. For example, the formatting of bolded subheadings in Section 3.2 is inconsistent. There is an extra comma at the start of line 200. The \"CMF\" in Eqn.3 should be \"CFM\". \n\n2、The architecture of OmniTalker heavily relies on a dual-branch DiT framework.\n\n3、 The ablation study only evaluates different settings of Audio-Video Fusion.\n\n4、The technical contributions are not clear. \n\n5、The symbols in Eqn. 3 and Eqn.4 lack explanations.", "was_revised": true, "final_justification": "I have considered rebuttal and discussions with authors, other reviewers and AC. I raise my score to 4 (borderline accept).", "rebuttal": "# Response to Reviewer YtbX\n\nWe sincerely appreciate your constructive feedback and thoughtful suggestions. In response to your queries, we have provided detailed answers to each point. \n\n## Q1&2\nThank you for pointing out this issue. We appreciate the opportunity to further elaborate on our three core contributions mentioned in Sec. 1(Line 50)：\n\n1. **Unified Multi-Modal Generation with Temporal Interaction.** While our work adopts a dual-branch DiT framework, OmniTalker fundamentally differs from multi-modal conditional generation approaches (e.g., SD3, Flux, MMAudio) that treat text as a semantic condition. These methods focus on multi-modal input to single-modal output (e.g., text-to-image/audio), emphasizing semantic-level comprehension and alignment. In contrast, OmniTalker introduces a novel paradigm for multi-modal generation (audio-visual) where text-to-speech synthesis and video generation are tightly coupled through cross-modal temporal interaction. This ensures synchronized, fine-grained coordination between audio and visual modalities during generation.\n2. **Holistic Replication of Speaking Style via Dual-Modal Co-Generation.** Existing solutions often address voice cloning or facial expression synthesis independently, neglecting the interplay between vocal characteristics and visual dynamics. OmniTalker is the first to fully replicate the dynamic triad of speaking style (e.g., vocal timbre and prosody, head motion patterns, and facial expression dynamics) through joint audio-visual generation. By modeling these modalities as an integrated system, our framework preserves the nuanced correlations between speech prosody and facial movements (e.g., lip sync, eyebrow raises, nodding and shaking), achieving a more lifelike and personalized talking head synthesis.\n3. **Real-Time Inference with no latency.** While many methods prioritize generation quality at the cost of efficiency, OmniTalker achieves real-time performance (25 FPS) on a single NVIDIA RTX 4090 GPU. This is critical for applications requiring low-latency interaction (e.g., virtual assistants). Unlike auto-regressive or cascaded pipelines that suffer from sequential delays, our dual-branch architecture enables parallel audio-visual generation with synchronized temporal resolution. This design reduces inference latency by 40% compared to state-of-the-art methods, while maintaining superior quality in style preservation and lip-sync accuracy (validated via benchmark metrics like E-FID, P-FID and Sync-C).\n\nOur ablation study was not sufficiently comprehensive, as Sec. 4.4 only evaluated performance variations across different fusion strategies. To address this limitation, we will supplement our analysis with modality ablation experiments that systematically remove specific sensory modalities. These additional experiments will quantitatively measure: 1) cross-modal interactions within the dual-branch architecture through comparative evaluation metrics, and 2) replication capabilities by assessing generative consistency in zero-shot scenarios.\n\n| Setting | WER $ \\downarrow $ | SIM-A $ \\uparrow $ | Sync-C $ \\uparrow $ | E-FID $ \\downarrow $ | P-FID $ \\downarrow $ |\n| --- | :---: | :---: | :---: | :---: | :---: |\n| Text only ($ \\alpha_{M_r}=0.0 $,$ \\alpha_{C_r}=0.0 $,$ \\alpha_{S_T}=2.0 $) | 1.65 | 0.249 | 4.66 | 0.87 | 4.21 |\n| w/o audio  ($ \\alpha_{M_r}=0.0 $,$ \\alpha_{C_r}=2.5 $,$ \\alpha_{S_T}=2.0 $) | 1.65 | 0.264 | 4.45 | 0.12 | 1.67 |\n| w/o video  ($ \\alpha_{M_r}=2.0 $,$ \\alpha_{C_r}=0.0 $,$ \\alpha_{S_T}=2.0 $) | 1.61 | **0.771** | 4.93 | 0.32 | 1.88 |\n| Full  ($ \\alpha_{M_r}=2.0 $,$ \\alpha_{C_r}=2.5 $,$ \\alpha_{S_T}=2.0 $) | **1.58** | 0.762 | **5.48** | **0.08** | **0.12** |\n\n\nThe table above compares the performance of four different settings. The full model (WER=1.58, Sync-C=5.48) outperforms all ablated variants, demonstrating that cross-modal integration of audio and video enhances text-to-speech generation accuracy and improves temporal alignment between modalities. When reference audio is not provided, the model cannot replicate the original speaker's vocal characteristics, as evidenced by a significant drop in SIM-A. However, the generated lip movements and head motions align naturally with the input text. Without reference video input, lip movements and head motions are primarily driven by audio loudness and textual content. Although synchronization remains coherent, person-specific details (e.g., unique facial expressions) are not preserved, resulting in significantly lower E-FID and P-FID scores. When only text is provided (without reference audio or video), the model stochastically generates a plausible speaking style, encompassing vocal tone, rhythm, emotional expression, lip motion, and head motion patterns. While speech articulation remains clear, the specific identity and style vary randomly. This highlights the effectiveness of the dual-branch architecture in enabling robust cross-modal learning and replication capabilities.\n\nThe slight drop in SIM-A when integrating video stems from two factors: (1) The training data contains regional accents and non-standard pronunciations, challenging acoustic modeling and timbre preservation. The model's reliance on pinyin/character-level alignments creates bottlenecks for capturing pronunciation variations deviating from canonical rules. (2) Early-stage training prioritizes speaker identity matching (SIM-A) as coarse timbre alignment is simpler than text-to-speech learning. During mid-training, focus shifts to refining text-to-speech alignment, causing SIM-A stagnation. Video integration improves prosody but introduces gradient conflicts, prioritizing visual-audio synchronization over fine timbre preservation. After retraining for 500k steps, we achieved a 0.02 SIM-A improvement on the Custom dataset. User studies confirm enhanced accent consistency, indicating the initial performance gap resulted from insufficient training convergence rather than architectural limitations.\n\n## Q3\nThank you for the detailed comments. We sincerely apologize for the confusion caused by the abbreviated notation and mistakes in our initial submission. To enhance clarity, we provide a detailed clarification of the symbols used in Eqn. 3 and 4 below.\nAs formulated in Eqn. 2, $ \\mathcal{L}_ {CFM} $ denotes the conditional flow matching loss. To enable balanced weighting of reconstruction losses between audio-visual representations, we decompose the CFM loss into modality-specific components, where $ \\mathcal{L}^m_{CFM} $ represents CFM loss for mel-spectrogram (detailed in Sec. 3.1, line 113) reconstruction and $\\mathcal{L}^h_{CFM}$, $ \\mathcal{L}^f_{CFM} $, $ \\mathcal{L}^e_{CFM} $ correspond to head pose, facial expression blendshapes, and eye movement coefficients in visual codes respectively (detailed in Sec. 3.1, line 116). The weighting coefficients $ \\lambda_m $,$ \\lambda_h $,$ \\lambda_f $,$ \\lambda_e $ control the contribution of each loss component, with values specified in Sec. 4.1 (line 217) as $ \\lambda_m=0.1 $, $ \\lambda_h=0.5 $, $ \\lambda_f=3.0 $,  $ \\lambda_e=0.5 $.\nEqn. 4 presents the vector field computation under Classifier-Free Guidance (CFG) settings that manipulates the condition strength during the sampling process of conditional diffusion models. Here, $ v_t(x_0, c) $ represents the predicted velocity under condition $ c $($ c\\in C $, detailed in Line 198) at timestep $ t $ , while $ v_t(x_0, 0) $ corresponds to the unconditional vector field prediction when $ c=0 $. The associated CFG scale parameter $ \\alpha_c $ governs the contribution of the conditional component in this guidance framework. And we set $ \\alpha_{M_r}=2.0 $, $ \\alpha_{C_r}=2.5 $, $ \\alpha_{S_T}=2.0 $ as mentioned in Line 222. \nIn the final manuscript, we will thoroughly revise all writing-related errors to ensure linguistic accuracy and professionalism in the final submission.\n\n## Q4\nThank you for this observation. To enhance visual clarity, we focused on visualizing the **Yaw** axis, which represents the dominant rotational component in head motion (yaw, pitch, and roll). Figure 2 and the accompanying analysis highlight yaw angle dynamics, which are used to quantify head pose and serve as a critical component of the complex facial motion patterns in our study. We will highlight it in the final version. Here we further validate the significant impact of yaw angle on the speaking style of generated talking videos. As a critical component of speaking style, head pose manifests through rhythmic oscillations, semantic-related nodding/shaking gestures, and directional gaze behaviors. Our observations reveal inherent limitations in current SOTA talking head generation methods: they tend to produce frontal outputs with limited dynamic motion and fail to replicate reference head poses. Therefore, we select yaw as the evaluation metric to demonstrate our method's capability in understanding and imitating reference head pose characteristics. As shown in Figure 2 (reference on the left of the red dashed line while generated results on the right), the reference video exhibits 4 prominent rightward rotations (yaw>0). Comparative analysis reveals that baseline methods maintain near-static angles without dynamic variation, while StyleTalk duplicates the reference poses through direct parameter copying. Notably, our method achieves high consistency with the reference sequence in both movement amplitude and frequency, while preserving essential individual differences, thus demonstrating effective inheritance of head-pose style. Appendix B.3 presents additional eye movement replication results (see Figure 9), and Figures 7-8 provide visual comparisons of generated video frames. These evidences comprehensively validate the superiority of our approach in head pose generation.\n\nWe acknowledge that our initial explanation may not have been sufficiently comprehensive. All outstanding recommendations will be addressed in our final submission."}, {"review_id": "8IwVZpKbpX", "reviewer": "Reviewer_NKZk", "summary": "This paper presents OmniTalker, a novel and unified framework for generating synchronized audio and video of a talking head from text input. The method is one-shot, meaning it can mimic a person's identity and speaking style from a short reference video. The core of the architecture is a dual-branch Diffusion Transformer (DiT) that jointly generates audio (mel-spectrogram) and visual motion features. The key innovation is the use of a masked infilling strategy, which allows the model to learn multimodal styles (vocal characteristics, head motion, facial expressions) in-context, without requiring an explicit style encoder.\nThe paper is exceptionally strong, presenting a well-motivated and elegant solution to a challenging problem. The experimental results are comprehensive and demonstrate state-of-the-art performance, particularly in style preservation, where it vastly outperforms existing methods. The work is well-written, the architecture is clearly explained, and the claims are robustly supported by quantitative metrics, qualitative examples, and a user study. This is a high-quality submission that represents a significant advance in the field of talking head generation.", "questions": "1. Could you clarify the exact input representation for the text projector? Does the model use raw text, or an intermediate format like phonemes? Given the challenge of generating mel-spectrograms directly from text, more detail on this part of the pipeline would be very helpful.\n2. Did you consider an end-to-end approach that generates video pixels directly, instead of intermediate motion representations? My concern is that the GAN-based renderer might be tightly coupled with the learned motion codes, potentially limiting their applicability with different rendering architectures and may perform worse when applying a difference alignment method to input video.\n3. Did you apply another pre-trained weights when you perform large-scale pre-training, or just train from scatch?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The authors proposed an elegant method to solve text-driven talking head generation task in a unified, end-to-end framework that jointly synthesis both modalities.\n2. The proposed method is effective, which show really good visually results that achieve high lip-sync and high rendering quality simultaneously, and achieve comparable quality in text-to-speech results with the sota tts methods.\n3. The proposed method is one-shot, only a reference video and a reference audio needed to generate same style results.\n4. The paper is well-writen and easy to follow.\n\nWeaknesses:\n1. The generated video and audio are rely on off-the-shelf video render and audio vocoder, which limite the output quality to upper-bound of the decoder they adopt.", "was_revised": true, "final_justification": "Based on its novelty and the interesting nature of the task, I recommend acceptance.", "rebuttal": "# Response to Reviewer NKZk\n\nThank you for your careful reading, helpful comments, and constructive suggestions. We have responded to each question individually. \n\n## Q1\nThat is a good question！As we stated in Sec. 3.1(Line 127)，the driving text and reference text undergo a unified tokenization process: Chinese text is converted into pinyin sequences, while Latin-based languages are represented as character/letter sequences. Specifically, we maintain a vocabulary list that includes symbols, digits, characters, pinyin sequences for Chinese characters, and additional elements (an illustrative example of a vocabulary list: [!, \", #, ..., 0, 1, 2, 3, ..., A, B, C, ..., ang1, ang2, ang4, ...]). These processed text sequences are subsequently transformed into integer indices using this vocabulary mapping. The resulting index sequences are then concatenated and zero-padded to align with the temporal length of the corresponding mel-spectrogram.\n\n## Q2\n\nYour concerns highlight critical considerations in video generation architecture design. We did explore the trade-offs between end-to-end pixel generation and motion representation-based approaches. While end-to-end approaches could theoretically simplify the pipeline, motion representations offer critical advantages:\n\n1. **Semantic modeling.** Motion codes explicitly encode temporal coherence and semantic motion dynamics, which are challenging to learn in an end-to-end manner, especially for long or complex sequences.\n2. **Modular design benefits.** By decoupling motion modeling from appearance synthesis, this architecture enables domain-agnostic generalization capabilities. The motion module demonstrates robust adaptability to novel styles, resolutions, and visual domains without requiring retraining.\n3. **Post-hoc manipulation.** Explicit motion representations facilitate fine-grained temporal controls (velocity adjustment, motion scaling, directional modification) and spatial alignment operations that remain challenging for end-to-end pixel generation frameworks.\n4. **Real-time efficiency**. The motion code architecture achieves significant computational advantages, with the motion gecharanerator and GAN-based renderer delivering low-latency performance crucial for real-time streaming applications.\n\nThat said, end-to-end approaches remain an active research area. Certainly, we are developing an end-to-end generative model that directly synthesizes video pixels, and have already achieved preliminary success.\n\nI completely agree with your concern. GAN-based renderers often tightly integrate motion modeling and rendering components, which can indeed create dependency on specific motion code representations and alignment strategies. To mitigate this, we propose three strategies:  \n1. **Motion Code Disentanglement.** We adopt facial Blendshape coefficients as our motion code representation - a widely adopted, semantically meaningful framework in facial animation. This choice ensures inherent interpretability while explicitly decoupling motion representation from the rendering pipeline.\n2. **Cross-Architecture Compatibility.** By leveraging a 3D Morphable Model (3DMM) as an intermediary framework, these Blendshape coefficients can be seamlessly converted into complementary facial representations such as 3D landmarks or Projected Normalized Coordinate Code (PNCC). This ensures compatibility with various warp-based rendering architectures (e.g., FOMM, HeadGAN, LivePortrait) that rely on spatial deformation cues. \n3. **Coarse-to-Fine Optimization Pipeline.** We develop a multi-stage optimization framework for fitting Blendshape parameters to input sequences: First, we train a dedicated model to extract facial Blendshape coefficients. Next, we perform a coarse optimization stage to estimate pose. Concurrently, we optimize a global identity coefficient to fully decouple identity and motion representations. Finally, we fine-tune both pose and facial Blendshape parameters to achieve optimal fitting accuracy. This hierarchical optimization framework integrates state-of-the-art landmark detection algorithms to improve the precision of motion code extraction, ensuring a strict separation between identity and motion parameters throughout the entire fitting process.\n\nOur current design prioritizes controllability, generalization, and robustness over the simplicity of end-to-end learning. While coupling between motion codes and the renderer exists, modular training and explicit motion modeling reduce this dependency. We believe this balance is critical for practical applications where editability and adaptability are as important as raw generation quality. Thank you for raising these points. They align with active research directions in our roadmap!\n\n\n## Q3\nDue to the greater technical challenges and computational complexity involved in collecting audio-visual synchronization data compared to pure audio data,  the size of audio-only datasets is typically 1–2 orders of magnitude larger than that of audio-visual datasets. Furthermore, text-to-phoneme alignment presents greater technical difficulties than phoneme-to-lip movement alignment during model training, necessitating extended training durations. To accelerate the training process, we initialize a portion of the audio branch's weights from the F5-TTS model, followed by end-to-end fine-tuning of the complete model on audio-visual data. The datasets and training strategy are detailed in Appendix B.1 and B.2.\n\nGiven the page constraints, we apologize for any unclear explanations. We will implement all remaining suggestions in our final version."}, {"review_id": "05xGrNh9ok", "reviewer": "Reviewer_Xaqr", "summary": "This paper introduces a new setting: given a reference video and a driving text prompt, the model can generate synchronized audio-visual talking heads while preserving the speaking style (both vocal and facial) of the reference video. To achieve this, the authors first employ a shared encoder to encode the text prompt, reference video, and audio, followed by two separate heads dedicated to generating audio and video, respectively. Additionally, they adopt a masked infilling strategy during training, which helps maintain each individual’s unique speaking style during generation. Notably, the proposed approach is capable of running in real time on a single GPU, and demonstrates strong audio-visual synchronization performance.", "questions": "1. How does this method perform in singing scenarios? \n2. Is it possible to generate results without a reference video? What happens if only audio or video is provided? Can the system still generate plausible outputs in those cases?\n3. How is the renderer trained? Are the 3D keypoints you use the commonly adopted 68 facial landmarks? If so, how do you ensure the motion consistency of features like hair and shoulders, which are not explicitly covered by those keypoints?\n4. Does the reported 24 FPS include the 3D face reconstruction step?\n5. Since your rendering module essentially operates as an image-based model, and the reference image is randomly selected, how do you ensure the consistency of features like teeth, which may not be clearly visible in all reference images?\n6. Given that many video generation methods based on Stable Diffusion tend to degrade when generating long videos due to overlapping inference artifacts, can this method generate long videos without such degradation?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. They are the first to introduce a novel setting: given a reference video and a driving text, the system is able to generate synchronized audio-visual talking heads.\n2. The generated video preserves the speaking style of the reference, including both vocal characteristics and facial expressions.\n3. The proposed method achieves real-time inference at 25 FPS on a single GPU.\n4. The generated results exhibit high-quality audio-visual synchronization.\n\n\nWeakness:\n1.  This paper uses a 3DMM model as the motion representation, which inherently limits the method to the scope of 3DMM. For example, it can only handle real human faces, and lacks the flexibility of former diffusion-based approaches （\\eg, ominihuman, loopy) that can generalize to a wider range of scenarios, such as AIGC-generated avatars, cartoons, or animals. Additionally, 3DMM reconstructions can become inaccurate under large head poses.\n2. The paper’s claim of being end-to-end seems somewhat misleading. In fact, the audio and video generators are first pre-trained separately, and the DiTs are only used later to generate intermediate representations. This setup is quite similar to previous methods such as Float and VASA-1.\n3. The paper lacks comparisons with other real-time and open-source methods, such as JoyVASA and DiTTO, which should be included for a more comprehensive evaluation.\n4. Some training details are missing. For instance, how is the renderer trained? What exactly do the “3D keypoints” represent? Since 3DMM only accounts for the facial region, how is the dynamic behavior of hair ensured during generation?", "was_revised": true, "final_justification": "The author's response addresses some of my concerns. I decide to maintain my original score.", "rebuttal": "# Response to Reviewer Xaqr\n\nThank you for your expert feedback and suggestions. We have carefully considered each of your points and provided responses accordingly.\n\n## Q1\n\nThis is a highly relevant question! We have indeed experimented with using singing segments as reference clips, but the results are not yet satisfactory. While the model maintains strong capabilities in timbre cloning and phonetic clarity (accurately preserving vocal characteristics and text alignment), it faces significant challenges in generating musically coherent outputs. Generated results often exhibit fragmented rhythms, unnatural chord progressions, and mismatched background music (BGM), leading to a lack of harmonic cohesion between vocals and accompaniment.\nOn the one hand, our training datasets lack high-quality singing samples with accompaniment. Audio containing background music often scores poorly on DNSMOS (a speech quality metric) and was filtered out during preprocessing according to Appendix B.1. This limits the model's  ability to learn musical structures like melody-lyric alignment and rhythmic phrasing. On the other hand, singing scenarios demand modeling multi-dimensional dependencies (text, pitch, tempo, BGM), which is far more complex than text-to-speech (TTS). With only reference audio and text as inputs, the model struggles to infer musical intent (e.g., melody direction, harmonic context) without explicit rhythmic or BGM guidance.\nTo address these challenges, we plan to:\n1. **Add Musical Control Signals**. Introduce explicit inputs like tempo (BPM), rhythmic patterns, chord sequences, or MIDI-like annotations to guide musical structure generation.\n2. **Expand Training Data**. Collect a singing-TTS dataset with multimodal annotations (e.g., melody contours, rhythm labels) to strengthen supervision on musical elements.\n3. **Explore staged training**. First optimizing vocal content (timbre, lyrics, pitch) and then refining background music generation separately to improve overall coherence.\n\nWhile singing generation is not currently prioritized in our open release, we are actively iterating on this capability. We look forward to sharing progress soon! \n\n## Q2\n\nThat's an excellent question that highlights additional user cases. Thanks to our training strategy as mentioned in Appendix B.2, which involves randomly dropping text, audio, or video conditioning inputs with a certain probability, our model inherently supports unconditional generation. Here's how it works in different scenarios:\n1. **Text-only input**. When only text is provided (without reference audio/video), the model stochastically generates a plausible speaking style, including vocal tone, rhythm, emotional expression, lip motion, and head motion patterns. While the speech articulation remains clear, the specific identity and style will vary randomly.\n2. **Audio-only input**. With only audio input, the lip movements and head motions are primarily influenced by the audio loudness and text content. While the synchronization stays coherent, person-specific details (e.g., unique facial expressions) will not be preserved.\n3. **Video-only input**. When only video is provided, the model cannot replicate the original speaker's vocal characteristics. However, the generated lip movements and head motions will align naturally with the input text.\n\nIn all cases, the outputs maintain logical coherence and visual/audio alignment, though specific identity traits may be lost when certain modalities are absent. This flexibility enables diverse applications while preserving core functionality.\n\n## Q3\n\nThe renderer we use is a warp-based GAN generator trained via a self-reenactment framework as described in Appendix A.2. Specifically, we first extract the appearance features from source image, and then leverage 3D facial landmarks to drive the transformation of expression and pose from the source to the target through a warping module. The final image is synthesized by a SPADE-based generator that effectively fuses the warped features with spatially adaptive normalization.\n\nRegarding the 3D landmarks, we manually select a set of key points from the 3DMM model that are representative of facial expressions and head pose—these are not limited to the standard 68 2D facial landmarks, but are instead chosen to optimally capture 3D facial dynamics. While these keypoints primarily correspond to facial structures and do not explicitly cover regions such as hair or shoulders, the network learns to implicitly extend the influence of each keypoint during training. This allows nearby non-facial regions (e.g., hair and upper shoulders) to be co-driven in a spatially coherent manner, ensuring motion consistency across the entire head and surrounding areas. The learned deformation field effectively generalizes the control beyond the immediate vicinity of the keypoints, enabling natural and holistic head motion rendering.\n\n## Q4\n\nThe reported 24 FPS does not include the 3D face reconstruction step. Given a reference video, we first perform 3D face reconstruction as a preprocessing step, which is executed only once. During the _inference_ phase, instead of conducting reconstruction, we utilize the 3DMM model to convert the output motion blendshape parameters into facial landmark coordinates. These landmarks serve as control signals for the subsequent GAN-based model. The vertex coordinate calculations involved in this conversion process incur negligible computational overhead on the GPU. \n\n\n## Q5\n\nWe would like to clarify the use of randomly selected reference images in our rendering framework. The random selection strategy is primarily employed during the _training_ phase of the renderer to enhance the model's robustness and generalization capability. Indeed, we observe that the choice of reference image—particularly whether it shows teeth or open eyes—can influence the visual details in the generated output.\n\nHowever, during the _inference_ stage in practical application, we do not select reference images randomly. Instead, we adaptively choose a reference frame from the input reference video based on facial expression analysis. Specifically, to ensure consistent and realistic rendering of features such as teeth, we select a reference image in which the subject's mouth is open and teeth are clearly visible. This strategy ensures that the renderer produces temporally coherent and identity-consistent results, including accurate appearance of internal facial structures like teeth, while maintaining natural motion dynamics. \n\n\n## Q6\n\nGreat question！Diffusion-based models employ a fixed-length generation mechanism. To ensure pixel-level continuity across different inference clips, they require not only appearance reference frames but also temporal reference frames. However, this temporal reference frame integration can lead to error accumulation during long-sequence inference (because of the gap of temporal reference frames between _training_ and _inference_ phases). Instead of directly generating pixels, our diffusion model only generates mel-spectrograms (for audio) and motion codes (for video), which allows for seamless transitions via simple fade-in/fade-out smoothing rather than complex pixel-level alignment. The GAN-based renderer handles pixel generation by warping the appearance of reference frames according to motion codes frame-by-frame without temporal reference frames during _inference_. This design decouples audio-visual coherence from pixel complexity, enabling scalable long video synthesis while maintaining fidelity. We successfully generated 30-minute videos with consistent quality, showing no visible artifacts or quality loss.\n\nWhile we have addressed each of your questions in our responses, we must apologize for any remaining ambiguities due to the limited space. Please rest assured that we will incorporate all remaining suggestions in our final submission."}, {"review_id": "qjQ9Uem8de", "reviewer": "Reviewer_7PYd", "summary": "This paper introduces OmniTalker, the first unified framework for one-shot real-time talking head generation based on text. By developing a DiT-based backbone with audio-video fusion, OmniTalker generates high-quality synchronous video and audio that mimics the styles of the reference. The contributions include a dual-branch DiT-based backbone, a unified framework for training and one-shot generation, and a whole system that could generate at a high frequency of 25Hz. The framework is evaluated on publicly available datasets and custom datasets, compared with TTS and audio-driven methods, and shows improvements. This work provides a prototype for real-time text-driven talking head generation and provides further study directions.", "questions": "1.  Could the authors clarify whether they have a plan to release the custom dataset?  This will increase the soundness and the influence of this work.\n\n2. In Table 1, OmniTalker underperforms previous work on SEED dataset. Could the authors give a further discussion on this? Additionally on Custom dataset, OmniTalker gives the best result. It is explained in the paper that the video could also help improve audio performance. However, why  SIM-A with $\\alpha_{C_r}=2.5$ is worse on the Custom dataset?\n\n3. In Related Work section, line 62, it is said a detailed comparison with prior methods is included in the Appendix. However the paper do not include an appendix and I did not find it in the supplementary material as well.\n\n4. How is the hyperparameters, like block size, determined?", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This work is the first one-shot real-time text-driven talking head generation framework and fill the gap in this area.\n2. The entire pipeline of the framework is well demonstrated, providing a clear methodology for further research in this area.\n\nWeakness:\n1. Lack of comparison with existing text-driven THG baselines (e.g., TTSF [1] referred to in related works).\n2. The custom dataset is not available, and the results are hard to check. Although the authors claim the availability of the dataset, I did not find a link to the dataset. \n3. The ablation is not adequate, and the effectiveness of CFG and dual-branch DiT is not fully validated.\n\n[1] Jang, Youngjoon, et al. \"Faces that speak: Jointly synthesising talking face and speech from text.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "was_revised": true, "final_justification": "My final recommendation for this paper is acceptance. All my questions are addressed by the authors. \n### Q1. Question about whether the authors will release the custom dataset\nThe authors promise to release their dataset in 2025 Q4, and provided a timeline for this.\n### Q2. Question about the performance of OmniTalker\nThe authors explained the reasons and future research plans.\n### Q3. Question about missing section.\nThis section is added in the revised manuscript and in comment.\n### Q4. Question about hyperparameters.\nThis is explained in the response.", "rebuttal": "# Response to Reviewer 7PYd\n\nThank you for your valuable feedback and suggestions. We have prepared individual responses to each of your questions. \n\n## Q1\n\nYes, we have plans to release the custom dataset. We are currently finalizing the necessary steps, including ethical reviews and data curation, and aim to make it publicly available by Q4 2025. \n\n## Q2\n\nThank you for your insightful questions. We appreciate the opportunity to elaborate on these points: \n1. **WER performance.** Our training dataset maintains a 3:1 ratio of Mandarin to English data. This imbalance likely contributes to the disparity in performance between SEED-ZH/Custom (where we achieve the best WER) and SEED-EN (where F5-TTS outperforms us). The limited English data constrains the model's ability to generalize to English pronunciation patterns, particularly in challenging acoustic scenarios. We are actively exploring data augmentation and rebalancing strategies to address this limitation in future work. \n2. **SIM-A performance.** The suboptimal SIM-A performance arises from two factors: (1) the training dataset contains regional accents and non-standard pronunciations, which challenge both acoustic modeling and timbre preservation. Our model's reliance on pinyin or character-level alignments may create bottlenecks in capturing these variations, especially when they deviate from canonical pronunciation rules. (2) We found that during early-stage training, the model tends to prioritize speaker identity matching (SIM-A), as coarse timbre matching is simpler than learning precise text-to-speech alignments. However, in the mid-training phase, as the model refines text-to-speech alignment, its focus shifts away from optimizing SIM-A, causing this metric to plateau. Additionally, while video integration improves prosody, it introduces gradient conflicts. The model prioritizes visual-audio synchronization over fine-grained timbre preservation, resulting in a slight degradation in SIM-A performance.\n\nWe have retrained our model for an additional 500k steps and observed a 0.02 improvement in SIM-A on the Custom dataset. User studies confirm enhanced accent consistency, suggesting that the original performance was primarily due to insufficient training convergence rather than a fundamental limitation of the model architecture.\n\nWe have conducted additional experiments to validate the efficacy of the Dual-Branch DiT architecture, and the preliminary results (shown in the attached table) align with our original conclusions. These will be included in the revised paper.\n\n| Setting | WER $ \\downarrow $ | SIM-A $ \\uparrow $ | Sync-C $ \\uparrow $ | E-FID $ \\downarrow $ | P-FID $ \\downarrow $ |\n| --- | :---: | :---: | :---: | :---: | :---: |\n| Text only ($ \\alpha_{M_r}=0.0 $,$ \\alpha_{C_r}=0.0 $,$ \\alpha_{S_T}=2.0 $) | 1.65 | 0.249 | 4.66 | 0.87 | 4.21 |\n| w/o audio  ($ \\alpha_{M_r}=0.0 $,$ \\alpha_{C_r}=2.5 $,$ \\alpha_{S_T}=2.0 $) | 1.65 | 0.264 | 4.45 | 0.12 | 1.67 |\n| w/o video  ($ \\alpha_{M_r}=2.0 $,$ \\alpha_{C_r}=0.0 $,$ \\alpha_{S_T}=2.0 $) | 1.61 | **0.771** | 4.93 | 0.32 | 1.88 |\n| Full  ($ \\alpha_{M_r}=2.0 $,$ \\alpha_{C_r}=2.5 $,$ \\alpha_{S_T}=2.0 $) | **1.58** | 0.762 | **5.48** | **0.08** | **0.12** |\n\n\nThe table above compares the performance of four different settings. The full model (WER=1.58, Sync-C=5.48) outperforms all ablated variants, demonstrating that cross-modal integration of audio and video enhances text-to-speech generation accuracy and improves temporal alignment between modalities. When reference audio is not provided, the model cannot replicate the original speaker's vocal characteristics, as evidenced by a significant drop in SIM-A. However, the generated lip movements and head motions align naturally with the input text. Without reference video input, lip movements and head motions are primarily driven by audio loudness and textual content. Although synchronization remains coherent, person-specific details (e.g., unique facial expressions) are not preserved, resulting in significantly lower E-FID and P-FID scores. When only text is provided (without reference audio or video), the model stochastically generates a plausible speaking style, encompassing vocal tone, rhythm, emotional expression, lip motion, and head motion patterns. While speech articulation remains clear, the specific identity and style vary randomly. This highlights the effectiveness of the dual-branch architecture in enabling robust cross-modal learning and replication capabilities.\n\n\n## Q3\n\nThank you for pointing out the omission in Sec. 2. We sincerely appreciate your careful reading and constructive feedback. We will add a detailed discussion in Appendix in the revised manuscript as follows: \n\n| Method | text driven | one-shot | voice clone | style replicate | real-time |\n| --- | :---: | :---: | :---: | :---: | :---: |\n| Wav2Lip | | &#10003; | | | &#10003; |\n| VividTalk | | &#10003; | | | &#10003; |\n| SadTalker | | &#10003; | | | &#10003; |\n| StyleTalk | | &#10003; | | &#10003; | |\n| VASA-1 | | &#10003; | | | &#10003; |\n| EchoMimic | | &#10003; | | | |\n| Hallo | | &#10003; | | | |\n| TTSF | &#10003; | &#10003; | | | &#10003; |\n| AV-Flow | &#10003; | | &#10003; | &#10003; | &#10003; |\n| **Ours** | &#10003; | &#10003; | &#10003; | &#10003; | &#10003; |\n\n\n## Q4\n\nIn determining the model scale, we primarily consider two critical factors: model capacity and the computational requirements dictated by real-time performance constraints. Considering the data scale, the input text has a vocabulary size of approximately 2.5k, while the audio/visual representations consist of 100D mel-spectrograms and 60D motion codes. To balance model capacity with real-time inference efficiency, we set the hidden dimensions to 512/1024 for text/audio-visual embeddings. Building upon insights from state-of-the-art TTS models like F5-TTS, the architecture is carefully calibrated to optimize parameter efficiency within a total parameter budget of approximately 0.8 billion, achieving a balanced trade-off between computational resources and performance.\n\nWe regret any lack of clarity in our initial submission due to the constraints of space and the volume of revisions. We will incorporate all remaining suggestions in our final submission."}]

Author rebuttal:

Meta-review: This paper introduces the first one-shot real-time framework (25FPS on a single GPU) for text-driven talking head generation, which jointly synthesizes speech and synchronized facial motion via a dual-branch DiT with cross-modal fusion. The reviewers in general found the work novel, the design elegant, and the style preservation performance strong, which led to their consensus to accept the paper after the rebuttal. The remaining concerns include limited ablations, reliance on the off-the-shelf vocoder and renderer, missing baselines, underperformance on English-heavy datasets, and lack of dataset release. However, the authors addressed most of these concerns in the rebuttal, and the real-time generation of talking heads seems like a timely and significant advance for talking head generation. Thus, I believe that the paper is a clear accept.
Decision: Accept (poster)