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ApoAvatar: Expressive Audio-Driven Avatar Generation via Refocused Audio-Pose Priors

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.6Download Report PDF
Video GenerationAudio Driven Avatar Animation
Abstract:

Audio-driven human video generation has greatly improved lip synchronization. However, most methods still use audio mainly to control the mouth, while the relationship between speech rhythm and body motion remains weak. This often makes generated characters look unnatural. We present \textbf{ApoAvatar}, a diffusion-based framework that ties speaking style to motion dynamics. We introduce an Audio–Pose Prior Refocusing mechanism, which adjusts pose guidance based on audio intensity. Strong accents increase gesture magnitude, while quiet parts suppress unnecessary motion. We also design a frame-wise audio–video interaction module. It updates audio features using the current visual context and the refocused pose prior through a designed bidirectional cross-attention. This yields better short-term synchronization and motion coherence. The framework supports both pose-controlled and pose-free inference within one model. Extensive experiments on EMTD and HDTF show clear gains over strong baselines in lip–audio synchronization, gesture expressiveness, and overall motion naturalness.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

ApoAvatar proposes a diffusion-based framework that couples speech rhythm with body motion dynamics through an Audio-Pose Prior Refocusing mechanism and frame-wise audio-video interaction. The paper resides in the Multi-Stage and Controllable Video Generation leaf under Video-Based Avatar Generation, which contains six papers including the original work. This leaf represents a moderately populated research direction focused on multi-stage pipelines and explicit control mechanisms. The taxonomy shows that video-based avatar generation is a substantial branch with multiple specialized leaves, indicating active research interest in photorealistic synthesis with controllable attributes.

The taxonomy reveals that ApoAvatar sits adjacent to several related directions. The Diffusion-Based Full-Body Generation leaf (six papers) focuses on holistic motion dynamics with emotion control, while Real-Time and Efficient Video Synthesis (two papers) prioritizes low-latency generation. The Asynchronous and Decoupled Video Generation leaf (one paper) explores separate facial and body streams. ApoAvatar's emphasis on audio-intensity-driven pose adjustment and bidirectional cross-attention distinguishes it from purely facial methods in the Facial and Head Animation branch, which excludes full-body gesture synthesis. The taxonomy's scope notes clarify that multi-stage controllable methods differ from single-stage real-time approaches by trading latency for expressiveness and control granularity.

Among nineteen candidates examined across three contributions, no clearly refuting prior work was identified. The Audio-Pose Prior Refocusing mechanism examined ten candidates with zero refutable matches, suggesting limited direct overlap in the specific approach of dynamically adjusting pose guidance based on audio intensity. The Frame-Wise Audio-Video Interaction strategy examined only two candidates, reflecting a narrower search scope for this component. The overall ApoAvatar framework examined seven candidates without refutation. These statistics indicate that within the limited search scope, the proposed mechanisms appear distinct from examined prior work, though the small candidate pool (nineteen total) means the analysis does not cover the full breadth of related literature.

The analysis suggests moderate novelty within the examined scope, particularly in the audio-intensity-driven pose refocusing mechanism. However, the limited search scale (nineteen candidates from top-K semantic search) means substantial related work may exist outside this sample. The taxonomy context shows ApoAvatar occupies a moderately crowded research area with multiple competing approaches to controllable video generation, suggesting incremental rather than transformative contributions. A more exhaustive literature review would be needed to definitively assess novelty across the broader field of audio-driven avatar synthesis.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
19
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Audio-driven expressive avatar generation with synchronized body motion. The field encompasses methods that transform speech or audio signals into realistic animated avatars, spanning facial expressions, head movements, and full-body gestures. The taxonomy organizes this landscape into several major branches: Full-Body Motion Synthesis focuses on generating coordinated gestures and body dynamics from audio (e.g., Audio2AB[3], ExpGest[5]), often addressing the challenge of producing natural, speech-synchronized movements beyond the face. Facial and Head Animation targets lip-sync, expression modeling, and head pose generation (e.g., Audio2Head[1], Speech2UnifiedExpressions[16]), emphasizing fine-grained control over facial features. Video-Based Avatar Generation leverages diffusion models and multi-stage pipelines to produce photorealistic talking-head videos (e.g., CyberHost[4], Emo2[17]), balancing visual quality with temporal coherence. Specialized Applications and Domains explore niche settings such as virtual teachers, robotic embodiments, or emotion-driven scenarios, while Foundational Techniques and Analysis provides the underlying architectures, loss functions, and evaluation metrics that support these diverse methods. Recent work reveals a tension between holistic realism and modular controllability: some approaches pursue end-to-end video synthesis for maximum photorealism (Stereo-Talker[21], MoCha[22]), while others decompose the problem into separate facial, head, and body modules for finer artistic control (Versatile Multimodal Controls[23]). ApoAvatar[0] sits within the Video-Based Avatar Generation branch, specifically in the Multi-Stage and Controllable Video Generation cluster, where it emphasizes orchestrating multiple generation stages to achieve both expressive body motion and high-fidelity visual output. Compared to nearby works like Emo2[17], which prioritizes emotional expressiveness in talking faces, or Unit Enhancement Guidance[50], which refines intermediate representations for better lip-sync, ApoAvatar[0] appears to integrate body-motion synthesis more tightly with video rendering, aiming for a unified pipeline that maintains synchronization across modalities. This positioning reflects broader trends toward multi-modal coherence and user-controllable generation in avatar systems.

Claimed Contributions

Audio-Pose Prior Refocusing mechanism

This mechanism computes frame-level prosodic intensity from audio to dynamically adjust pose guidance. It ensures gesture strength matches speaking style by increasing motion magnitude during strong accents and suppressing unnecessary motion during quiet parts.

10 retrieved papers
Frame-Wise Audio-Video Interaction strategy

This module updates audio features through bidirectional cross-attention using current visual context and refocused pose prior. It produces pose-aware and video-aware audio embeddings that strengthen audio-motion coupling and improve short-term synchronization.

2 retrieved papers
ApoAvatar diffusion-based framework

ApoAvatar is a unified framework that addresses weak audio-motion coupling in avatar generation by explicitly modeling the relationship between speech rhythm and body motion. The framework supports both pose-controlled and pose-free inference within one model.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Audio-Pose Prior Refocusing mechanism

This mechanism computes frame-level prosodic intensity from audio to dynamically adjust pose guidance. It ensures gesture strength matches speaking style by increasing motion magnitude during strong accents and suppressing unnecessary motion during quiet parts.

Contribution

Frame-Wise Audio-Video Interaction strategy

This module updates audio features through bidirectional cross-attention using current visual context and refocused pose prior. It produces pose-aware and video-aware audio embeddings that strengthen audio-motion coupling and improve short-term synchronization.

Contribution

ApoAvatar diffusion-based framework

ApoAvatar is a unified framework that addresses weak audio-motion coupling in avatar generation by explicitly modeling the relationship between speech rhythm and body motion. The framework supports both pose-controlled and pose-free inference within one model.

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