Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency

Authors: Jianwen Jiang, Chao Liang, Jiaqi Yang, Gaojie Lin, Tianyun Zhong, Yanbo Zheng
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Diffusion Model, Avatar, Portrait Animation, Audio-Condition Video Generation

Abstract:
With the introduction of video diffusion model, audio-conditioned human video generation has recently achieved significant breakthroughs in both the naturalness of motion and the synthesis of portrait details. Due to the limited control of audio signals in driving human motion, existing methods often add auxiliary spatial signals such as movement regions to stabilize movements, which compromise the naturalness and freedom of motion. To address this issue, we propose an end-to-end audio-only conditioned video diffusion model named Loopy. Specifically, we designed two key modules: an inter- and intra-clip temporal module and an audio-to-latents module. These enable the model to better utilize long-term motion dependencies and establish a stronger audio-portrait movement correlation. Consequently, the model can generate more natural and stable portrait videos with subtle facial expressions, without the need for manually setting movement constraints. Extensive experiments show that Loopy outperforms recent audio-driven portrait diffusion models, delivering more lifelike and high-quality results across various scenarios. Video samples are available at https://loopyavataranony.github.io/

Decision: accept (oral)
Reviewer scores: 8, 8, 8, 8

Reviews and discussion:
<thread>
creation date:
1730273642135

thread_type:
official_review

<thread_object>
creator: reviewer_exjv

<content>
summary:
This paper introduces an audio2video model for co-speech human portrait video synthesis. A novel temporal module is proposed to enable natural movement generation. A joint audio, movement, and expression latent space is learned to achieve better head pose and facial expression control from speech. Experiments and demonstrations show better performance and more realistic results.

strengths:
1. The results are good. 
2. The introduction of two modules (Temporal and Audio) is reasonable and interesting. Ablation study supports the benefits of these modules.

weaknesses:
1. Lack of ablation of stand-alone intra- / inter-temporal model. Is both of them necessary or only the inter-clip temporal layer is enough?
2.  The functionality of the Temporal Segment Model is unclear. Is it for capturing the appearance of the character under different expressions? If so, why (L478) longer motion frames lead to worse results?
3. Similar to the above issue. I watched the video samples of the ablated model. Seems to me the ablation of either part leads to similar degradations — lack of head pose variance and subtle expression. This makes me unclear about the different roles of the two proposed modules.

questions:
1. During inference what if motion frames are provided? How would they influence the results?
2. Can the overall head motion be controlled?
3. (L291) Is there any analysis of the strong correlation between the head movement and expression variances? Can the type of expression be controlled?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730539896397

thread_type:
official_review

<thread_object>
creator: reviewer_baag

<content>
summary:
This paper proposes an end-to-end audio-driven portrait video generation method. This method introduces an inter- and intra-clip temporal module and an audio-to-latent module to establish long-term natural correlations between audio and portrait movements. Many lifelike and impressive results are presented.

strengths:
1. The motivation is clear. The authors focus on the weak correlation between audio and portrait motion in end-to-end audio-driven methods.
2. Overall, this paper is easy to follow. The proposed TSM module is technically sound in its design, and the experimental validation is effective.
3. Many synchronized and vivid portrait videos are generated.

weaknesses:
1. In the A2L module, the effects of Movement and Expression on the method have not been thoroughly validated. The audio inputs shown in Fig. 4 are somewhat confusing. I assume they refer to audio features from wav2vec. 
2. Human expressions are closely related to many facial details, but the implementation in the paper is rather trivial. 
    1) the detected landmarks are too sparse and not accurate enough (DWPose), which makes it difficult to capture a person's expression accurately. 
    2) using the variance of keypoints to calculate head movement and expression changes presents several practical issues, 
such as the entanglement of head movement and camera movement. Why not use FLAME coefficients or results from other emotion estimation methods? 
3. The TSM module needs a deeper discussion on its impact on overall computational efficiency.
4. In Tables 1 and 2, the methods perform worse than others on some metrics, especially those related to Glo and Exp. The authors do not provide detailed analysis or discussion on this.
5. The paper has several writing issues. Some symbols and abbreviations are introduced without explanation, such as TSM in Fig. 2. Additionally, some text in the figures is too small to read, such as "other computational layers" in Fig. 3. The main paper does not reference Table 2. There are also some typos, such as in Line 302, where there is an error with punctuation.
6. The paper does not include a discussion of the limitations of the proposed method.

questions:
1. Currently, end-to-end audio-driven portrait generation is typically trained on training sets of varying sizes, which is crucial for a good model. How can we reasonably evaluate the performance of the method?
2. In Table 3, the metrics for audio-visual synchronization related to Loopy w/o TSM and w/o ASL still outperform other methods. Does this indicate that the performance improvement of the method primarily comes from the self-collected data?
3. Regarding training A2L, how do head movements and expressions individually affect the results?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730752506279

thread_type:
official_review

<thread_object>
creator: reviewer_2ghy

<content>
summary:
The paper presents Loopy, an innovative audio-driven diffusion model for generating portrait videos that addresses limitations in current methods related to motion naturalness and dependency on auxiliary spatial signals. Existing approaches often compromise the natural freedom of movement by using preset spatial constraints like movement regions or face locators to stabilize motion, leading to repetitive and less dynamic results.

Loopy stands out by adopting an end-to-end audio-only conditioning framework, leveraging two main components: 1. Inter- and Intra-clip Temporal Modules: These modules are designed to extend the model’s temporal receptive field, enabling it to utilize long-term motion dependencies and generate consistent, natural motion across video frames without external movement constraints; 2. Audio-to-Latents Module: This module enhances the correlation between audio input and portrait motion by converting audio features and motion-related characteristics into latent space representations that guide the synthesis process.

Experiments show that Loopy outperforms existing methods, generating lifelike and stable videos with natural facial expressions.

strengths:
1. The paper introduces an end-to-end audio-only conditioned video diffusion model, which moves beyond traditional methods that rely on spatial constraints for motion stabilization.

2. The proposed novel modules like inter- and intra-clip temporal modules and audio-to-latents module are well-designed, resulting in more natural and consistent portrait movements and leading to better synchronization and more expressive facial movements.

3. The paper includes extensive experiments that demonstrate Loopy’s superiority over other audio-driven portrait diffusion models both quantitatively and qualitatively, with evidence of more lifelike and stable video outputs in the supplemental website.

4. The paper is well-written, the proposed components and architecture are described clearly.

weaknesses:
1. While the audio-to-latents module improves the audio-motion correlation, there is no mention of how different audio characteristics (e.g., background noise, varying loudness) might impact the model’s performance, which could be critical for real-world applications.

2. The paper lacks a detailed analysis of potential failure modes or scenarios where Loopy may struggle. Highlighting these cases would provide a more balanced view of the model's robustness and limitations.

questions:
1. Are there specific cases where Loopy struggles to maintain natural motion or facial expressions? An analysis of these limitations would provide a more complete understanding of the model’s strengths and weaknesses.

2. In the experiments section, the baseline models compared with Loopy were not trained using the collected dataset. It would be helpful to see how these baseline models perform when trained on the same dataset. This could further validate the effectiveness of the proposed modules and confirm that the performance gains are due to the model’s design, rather than advantages inherent to the dataset itself.

3. Will the collected dataset be made publically available?
</content>
</thread_object>

</thread>
<thread>
creation date:
1731301624554

thread_type:
official_review

<thread_object>
creator: reviewer_tnag

<content>
summary:
The paper proposes an audio-only conditioned video diffusion model. The model consists of three key components: an inter- and intra-clip temporal module, and an audio-to-latents module. These modules are designed to facilitate long-term movement modeling, enhancing the correlation between audio and motion. During inference, a single reference image as well as the audio is sent as input to autoregressively generate future frames window by window.

strengths:
1. The proposed method is solid, with enough technical contributions to address the long-term dependency between motions and audio conditions.

2. The experiment results are strong enough compared to prior works and baselines, in particular on FVD metrics and DExp metrics.

3. Both qualitative results and the demos shown in the supplementary webpage are appealing and convincing enough, where the long-term dependencies and correlations between audio and portrait motions are consistently maintained.

4. Overall, the paper is well-written and easy to follow, albeit having many technical details.

5. The human study results clearly show that the proposed method perceptually outperforms other baselines and prior arts.

weaknesses:
1. For audio-to-latent module, why replacing it with cross-attention module leads to largest performance drop as seen in Table 3. What are missing from cross-attention that makes it fail to perform as good.

2. During inference, audio ratio and ref ratio are manually set for classifier guidance, an ablation study is suggested to their impact on the final quality of generated video to have some insights about this weighting scheme.

3. Could the proposed method be further optimized and adapted to real-time settings, where the audio is being played and video follows interactively?

4. What are limitations of the proposed method and what could be improved? Are there failure cases where the generated motions cannot follow the audio closely?

questions:
See weaknesses above.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732426838018

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General Response to All Reviewers

comment:
We greatly appreciate the reviewers' positive comments on our proposed method, recognizing our contribution as good or even excellent, which is very encouraging for us. In our responses, we followed the reviewers' comments and provided additional experimental results and discussions. We believe these additions make the paper more complete and better support the effectiveness of our proposed modules.

---

**Regarding the method itself**

- We added a description of the current limitations in Appendix D and included a [video](https://loopyavataranony.github.io/video/rebuttal_video_limitation.mp4) describing these limitations on the provided project page.
- We provided more experimental results to support the effectiveness of our method, including comparisons between a simple baseline and the full model in Appendix C, and additional comparison [video](https://loopyavataranony.github.io/video/rebuttal_video_aba.mp4) on the provided project page showing the impact of the proposed modules.
- We also provided explanations and detection results ([video](https://loopyavataranony.github.io/video/rebuttal_video_expmov_det.mp4)) for expressions and movements in the audio-to-latents module, demonstrating their independent control over the generated results ([video](https://loopyavataranony.github.io/video/rebutall_video_expmov_control.mp4)), which is also included on the provided project page.
- We included discussions and experimental results on the impact of audio and reference CFG on the results in Appendix C.

**Regarding implementation details and deployment**

- We discussed the effect of TSM on inference speed, the current model's real-time factor (RTF) analysis, and potential real-time inference implementation in Appendix B.3, and the impact of motion frames during inference on the results in Appendix C.

We also revised the paper, including adjustments to the font size and module descriptions in Figures 2 and 3, and fixed the writing mistakes in the use of formulas, letters, and punctuation, making the paper easier to understand.

---

We are very grateful for all the valuable suggestions and positive recommendations from the reviewers. We believe the above additions regarding the method itself and its application implementation greatly enhance the completeness and value of the paper. We have carefully addressed the main concerns and provided detailed responses to each reviewer. We hope you will find the responses satisfactory. We would be grateful if we could hear your feedback regarding our answers to the reviews.


\
Sincerely

Authors of Paper 4292
</content>
</thread_object>

</thread>
<thread>
creation date:
1732543424893

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Dear AC and reviewers,

comment:
Firstly, we would like to express our sincere gratitude for organizing this conference and the review process. Based on the comments from reviewers, we have added more experimental results and included more visualizations and explanations to better support our method's effectiveness and the completeness of the paper. As the discussion period is nearing its end, we look forward to receiving feedback to ensure all concerns are fully addressed and are happy to clarify any remaining points.

Thank you once again for your time and effort.

\
Sincerely

Authors of Paper 4292
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper presents Loopy, an audio-driven portrait animation pipeline that demonstrates natural talking-head motion. The key innovation in Loopy against prior works is the use of diffusion-based synthesis using long-range motion frames that leads up to the currently synthesized frame, thus allowing access to the context for producing subtle, consistent, and continuous head movements. The model is trained on a very large dataset and experiments show state-of-the-art results. The provided videos show high-quality in talking head generation. 

The paper received four reviews, all recommending accept. The reviewers liked the quality of the generated videos, quality of the contributions, organization of the paper, and the extensive experimental results presented demonstrating state-of-the-art results.