ActAvatar: Temporally-Aware Precise Action Control for Talking Avatars

TL;DR

ActAvatar is a novel framework that achieves precise, temporally-aligned action control in talking avatars using text guidance, phase-aware attention, and hierarchical audio-visual alignment, outperforming existing methods.

Contribution

It introduces phase-aware cross-attention, hierarchical audio-visual alignment, and a two-stage training strategy for improved action control and lip synchronization.

Findings

Outperforms state-of-the-art in action control accuracy

Enhances lip synchronization quality

Maintains strong text-following capabilities

Abstract

Despite significant advances in talking avatar generation, existing methods face critical challenges: insufficient text-following capability for diverse actions, lack of temporal alignment between actions and audio content, and dependency on additional control signals such as pose skeletons. We present ActAvatar, a framework that achieves phase-level precision in action control through textual guidance by capturing both action semantics and temporal context. Our approach introduces three core innovations: (1) Phase-Aware Cross-Attention (PACA), which decomposes prompts into a global base block and temporally-anchored phase blocks, enabling the model to concentrate on phase-relevant tokens for precise temporal-semantic alignment; (2) Progressive Audio-Visual Alignment, which aligns modality influence with the hierarchical feature learning process-early layers prioritize text for establishing action structure while deeper layers emphasize audio for refining lip movements, preventing modality interference; (3) A two-stage training strategy that first establishes robust audio-visual correspondence on diverse data, then injects action control through fine-tuning on structured annotations, maintaining both audio-visual alignment and the model's text-following capabilities. Extensive experiments demonstrate that ActAvatar significantly outperforms state-of-the-art methods in both action control and visual quality.