UniLS: End-to-End Audio-Driven Avatars for Unified Listening and Speaking
Abstract
Generating lifelike conversational avatars requires modeling not just isolated speakers, but the dynamic, reciprocal interaction of speaking and listening. However, modeling the listener is exceptionally challenging: direct audio-driven training fails, producing stiff, static listening motions. This failure stems from a fundamental imbalance: the speaker's motion is strongly driven by speech audio, while the listener's motion primarily follows an internal motion prior and is only loosely guided by external speech. This challenge has led most methods to focus on speak-only generation. The only prior attempt at joint generation relies on extra speaker's motion to produce the listener. This design is not end-to-end, thereby hindering the real-time applicability. To address this limitation, we present UniLS, the first end-to-end framework for generating unified speak-listen expressions, driven by only dual-track audio. Our method introduces a novel two-stage training paradigm. Stage 1 first learns the internal motion prior by training an audio-free autoregressive generator, capturing the spontaneous dynamics of natural facial motion. Stage 2 then introduces the dual-track audio, fine-tuning the generator to modulate the learned motion prior based on external speech cues. Extensive evaluations show UniLS achieves state-of-the-art speaking accuracy. More importantly, it delivers up to 44.1\% improvement in listening metrics, generating significantly more diverse and natural listening expressions. This effectively mitigates the stiffness problem and provides a practical, high-fidelity audio-driven solution for interactive digital humans.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of UniLS, an end-to-end framework for generating unified speak-listen expressions in avatars, which significantly enhances the realism and interactivity of digital humans. The methodology is innovative, addressing a critical gap in the field, and the potential applications could have a lasting impact on how digital interactions are perceived and executed.
Methodology
The proposed UniLS framework introduces a two-stage training paradigm that innovatively addresses the challenge of generating realistic listening expressions in avatars. The first stage focuses on learning an internal motion prior without audio, which is a novel approach that captures the dynamics of natural facial motion. The second stage fine-tunes this prior using dual-track audio, allowing for a more responsive and realistic interaction between speaking and listening. This end-to-end design is a significant improvement over previous methods that relied on separate components, enhancing the potential for real-time applications.
Results & evaluation
The authors conducted extensive evaluations, demonstrating that UniLS achieves state-of-the-art performance in speaking accuracy and significantly improves listening metrics by 44.1%. The experiments are well-structured, comparing their method against existing benchmarks, though specific datasets and evaluation metrics are not detailed in the abstract. The results suggest a robust performance, but the paper would benefit from clearer descriptions of the datasets used and the statistical significance of the improvements.
Reproducibility
The paper lacks specific implementation details and code availability, which raises concerns about reproducibility. While the methodology is described, the absence of a demo or project URL limits the ability of other researchers to replicate the results. Providing access to the code and trained models would significantly enhance the reproducibility of the findings.
Limitations
One limitation is the reliance on dual-track audio, which may not always be available in practical scenarios. Additionally, while the method improves listening expressions, the paper does not address potential challenges in diverse speaking styles or accents, which could affect the generalizability of the model. The authors should also consider the computational demands of their approach, as real-time applications may require optimization.
Broader impact
The ability to generate lifelike avatars that can engage in natural conversations has significant implications for various fields, including virtual reality, gaming, and telecommunication. The advancements in modeling listening expressions can enhance user experiences in interactive digital environments, making digital humans more relatable and effective in communication. This work could pave the way for more sophisticated applications in education, therapy, and customer service. The main contribution of this paper is the introduction of UniLS, an end-to-end framework for generating unified speak-listen expressions in avatars, which significantly enhances the realism and interactivity of digital humans. The methodology is innovative, addressing a critical gap in the field, and the potential applications could have a lasting impact on how digital interactions are perceived and executed.
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