LES-Talker: Fine-Grained Emotion Editing for Talking Head Generation in Linear Emotion Space

TL;DR

LES-Talker introduces a highly interpretable fine-grained emotion editing model for talking head generation, leveraging a Linear Emotion Space based on Facial Action Units to enable detailed and controllable facial expression transformations.

Contribution

The paper proposes LES-Talker, a novel model that uses a Linear Emotion Space and a Cross-Dimension Attention Net to achieve interpretable, fine-grained emotion editing in talking head synthesis.

Findings

Outperforms mainstream methods in visual quality and emotion control.

Provides high interpretability and fine-grained emotion editing capabilities.

Successfully models complex emotion transformations with detailed facial control.

Abstract

While existing one-shot talking head generation models have achieved progress in coarse-grained emotion editing, there is still a lack of fine-grained emotion editing models with high interpretability. We argue that for an approach to be considered fine-grained, it needs to provide clear definitions and sufficiently detailed differentiation. We present LES-Talker, a novel one-shot talking head generation model with high interpretability, to achieve fine-grained emotion editing across emotion types, emotion levels, and facial units. We propose a Linear Emotion Space (LES) definition based on Facial Action Units to characterize emotion transformations as vector transformations. We design the Cross-Dimension Attention Net (CDAN) to deeply mine the correlation between LES representation and 3D model representation. Through mining multiple relationships across different feature and structure dimensions, we enable LES representation to guide the controllable deformation of 3D model. In order to adapt the multimodal data with deviations to the LES and enhance visual quality, we utilize specialized network design and training strategies. Experiments show that our method provides high visual quality along with multilevel and interpretable fine-grained emotion editing, outperforming mainstream methods.