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Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model
Fei Shen, Cong Wang, Junyao Gao, Qin Guo, Jisheng Dang, Jinhui Tang, Tat-Seng Chua
TL;DR
Long-term TalkingFace generation must maintain identity, head movement, expressions, and lip synchronization across extended sequences. MCDM combines archived and present motion priors with memory-efficient temporal attention, and experiments demonstrate effective identity and motion continuity while introducing a multilingual dataset.
Problem
Long-term TalkingFace generation remains challenged by coherent facial motion, audio-visual synchronization, expression diversity, and identity consistency across extended sequences.
Method
MCDM combines an archived-clip motion-prior for historical identity context, a present-clip motion-prior diffusion model for motion prediction, and memory-efficient temporal attention for long-sequence generation.
Results
Experimental results demonstrate MCDM's effectiveness in long-term TalkingFace generation, with robust identity consistency and motion continuity; the paper also introduces over 200 hours of multilingual video data.
Takeaways & Limitations
MCDM provides a unified approach for maintaining identity and temporal consistency in long-term TalkingFace generation while supporting evaluation with the TalkingFace-Wild dataset.
Takeaways & Limitations
The paper acknowledges ethical risks, including misuse for deceptive content or misinformation, and emphasizes transparency and detection frameworks.
Abstract
from arXiv · showhide
Recent advances in conditional diffusion models have shown promise for generating realistic TalkingFace videos, yet challenges persist in achieving consistent head movement, synchronized facial expressions, and accurate lip synchronization over extended generations. To address these, we introduce the \textbf{M}otion-priors \textbf{C}onditional \textbf{D}iffusion \textbf{M}odel (\textbf{MCDM}), which utilizes both archived and current clip motion priors to enhance motion prediction and ensure temporal consistency. The model consists of three key elements: (1) an archived-clip motion-prior that incorporates historical frames and a reference frame to preserve identity and context; (2) a present-clip motion-prior diffusion model that captures multimodal causality for accurate predictions of head movements, lip sync, and expressions; and (3) a memory-efficient temporal attention mechanism that mitigates error accumulation by dynamically storing and updating motion features. We also release the \textbf{TalkingFace-Wild} dataset, a multilingual collection of over 200 hours of footage across 10 languages. Experimental results demonstrate the effectiveness of MCDM in maintaining identity and motion continuity for long-term TalkingFace generation. Code, models, and datasets will be publicly available.
1. Introduction
TalkingFace generation must coordinate complex facial motion while preserving identity over extended sequences. MCDM addresses this with archived and present motion priors, temporal attention, and the multilingual TalkingFace-Wild dataset.
- Motivation: Extended TalkingFace generation must preserve identity while coordinating head, lip, and expression movements.These challenges arise from the complexity of facial movements and the need for identity consistency across long sequences.
- Motivation: Existing constraints can limit expression diversity, weaken audio-visual synergy, and produce rigid or incoherent long-term motion.Brief motion references are also insufficient for establishing coherent, dynamic movement.
- MCDM: MCDM combines archived-clip and present-clip motion priors with memory-efficient temporal attention for long-term consistency.The archived prior uses historical and reference frames, while the present prior predicts motion states and temporal attention updates historical features.
- MCDM: The archived-clip motion-prior enhances identity representation by constructing facial context from historical frames.It uses historical frames together with a reference frame through frame-aligned attention.
- MCDM: The present-clip motion-prior decouples current identity and motion features through multimodal causality and temporal interactions.It supports prediction of head, lip, and expression motion states.
- MCDM: Memory-efficient temporal attention dynamically integrates historical and current motion features to reduce error accumulation.The mechanism is designed to maintain temporal continuity during extended generation.
- Dataset: TalkingFace-Wild contains over 200 hours of video across 10 languages for TalkingFace research.The dataset broadens the available multilingual data for the task.
2. Related Work
TalkingFace methods have progressed from GAN-based intermediate motion representations to diffusion-based generation. MCDM differs by using archived and present motion priors to improve long-term motion prediction and identity consistency.
- GAN-Based Methods: GAN-based TalkingFace methods extract motion from audio or visual inputs and map it to intermediate representations before synthesis.Common representations include facial landmarks, 3DMM, and HeadNeRF.
- Diffusion-Based Methods: Diffusion-based methods produce high-quality, diverse TalkingFace videos while supporting synchronized lip, expression, and head-pose generation.Examples include AniPortrait, MegActor-Σ, Hallo, and EchoMimic.
- MCDM: MCDM introduces motion priors from both archived and present clips to improve long-term motion prediction and identity consistency.Historical frames and memory-efficient temporal attention support motion continuity while maintaining realism.
3. Method
MCDM combines archived identity context, present-clip motion prediction, and memory-efficient temporal attention to generate temporally coherent TalkingFace videos. Its modules address identity preservation, multimodal head/lip/expression motion, and error accumulation over long sequences.
- 3.1. Overall Framework: MCDM uses a denoising UNet with archived-clip, present-clip, and temporal attention pathways for conditional multi-frame generation.The architecture integrates archived and current motion priors before temporal feature updating.
- 3.2. Archived-Clip Motion-Prior Module: The archived-clip motion prior combines a reference frame with historical frames through frame-aligned attention to construct identity and motion context.Reference and archived frames are encoded into tokens, then processed frame by frame for efficiency on long sequences.
- 3.3. Present-Clip Motion-Prior Diffusion Model: The present-clip diffusion model predicts head, lip, and expression motion from audio and image tokens using multimodal causality and temporal interactions.Optional landmark tokens allow conditioning with or without reference video guidance.
- 3.4. Memory-Efficient Temporal Attention: Memory-efficient temporal attention replaces traditional temporal self-attention with fast attention to accumulate historical motion information while managing memory.Archived and present motion features are merged, and the motion memory is updated using current features and prior memory.
- 3.4. Memory-Efficient Temporal Attention: The temporal mechanism dynamically integrates historical and current motion features to reduce error accumulation during long-sequence generation.The method concatenates updated memory with present motion features along the temporal dimension.
4. Experiments
Experiments evaluate MCDM on established and newly introduced datasets using quantitative, qualitative, user-study, and ablation analyses. Across these evaluations, MCDM shows strong visual quality, synchronization, identity preservation, and long-term temporal consistency.
- Quantitative Evaluation: MCDM achieves the best scores across all metrics on HDTF and CelebV-HQ, including superior lip synchronization and temporal consistency.It obtains higher Sync-C, lower Sync-D, and strong SSIM and E-FID results than competing methods.
- Quantitative Evaluation: MCDM surpasses competing state-of-the-art methods on TalkingFace-Wild, achieving the best FID, FVD, and an E-FID of 1.97.The results indicate high-fidelity generation and temporal coherence across audio, expressions, and lip synchronization.
- Qualitative Evaluation: Qualitative comparisons show stronger head and lip synchronization, subtle facial actions, and identity consistency than several competing methods.The reported advantages include blinks, expression nuances, and overall visual results.
- User Study: In a study of 10 cases with 20 participants, MCDM receives the highest user-preference scores for identity consistency, motion synchronization, and video quality.Participants ranked shuffled videos from each method using rank-3 preferences.
- Ablation Study: Removing archived historical frames causes identity degradation, visible artifacts, and increasing error accumulation over longer generations.Without the archived-clip motion-prior, SSIM declines rapidly as frame count increases, whereas the full model remains more stable.
- Ablation Study: Removing the present-clip motion-prior or changing temporal-attention design choices degrades performance, while frame-aligned attention better captures temporal context and long-term dependencies.The present-clip prior supports multimodal motion prediction, and frame-aligned attention outperforms the Q-Former variant.
5. Conclusion
The paper presents MCDM for long-term TalkingFace generation, combining motion-prior modules and memory-efficient temporal attention to preserve identity and motion continuity. It also introduces a multilingual dataset exceeding 200 hours, while experiments report strong generation performance.
- Conclusion: MCDM targets robust identity consistency and motion continuity in long-term TalkingFace generation.Its three innovations are archived-clip motion priors, present-clip motion-prior diffusion, and memory-efficient temporal attention.
- Conclusion: Experimental results demonstrate MCDM’s effectiveness and report new benchmarks for long-term TalkingFace generation.
Impact Statement
The paper identifies ethical risks associated with TalkingFace generation, particularly misuse for deceptive content or misinformation. It emphasizes transparency and detection frameworks as safeguards.
- Impact Statement: MCDM may be misused to create deceptive content or spread misinformation, motivating transparency and detection frameworks.