# Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation

arXiv: https://arxiv.org/abs/2604.25255 · HTML: https://mlsanity.com/paper/2604.25255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 58 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Latent Image Animator: Learning to Animate Images via Latent Space Navigation](https://mlsanity.com/paper/2203.09043/) (2022)
- [Masked Vision and Language Modeling for Multi-modal Representation Learning](https://mlsanity.com/paper/2208.02131/) (2022)
- [Prompting Visual-Language Models for Dynamic Facial Expression Recognition](https://mlsanity.com/paper/2308.13382/) (2023)
- [CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition](https://mlsanity.com/paper/2303.00193/) (2023)
- [VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning](https://mlsanity.com/paper/2404.07078/) (2024)
- [Contextual Emotion Recognition using Large Vision Language Models](https://mlsanity.com/paper/2405.08992/) (2024)
- [Exploiting Temporal Audio-Visual Correlation Embedding for Audio-Driven One-Shot Talking Head Animation](https://mlsanity.com/paper/2504.05746/) (2025)
- [Learning Spatial-Temporal Coherent Correlations for Speech-Preserving Facial Expression Manipulation](https://mlsanity.com/paper/2604.20226/) (2026)

## Abstract (arXiv)

Speech-preserving facial expression manipulation (SPFEM) aims to enhance human expressiveness without altering mouth movements tied to the original speech. A primary challenge in this domain is the scarcity of paired data, namely aligned frames of the same individual with identical speech but different expressions, which impedes direct supervision for emotional manipulation. While current Visual-Language Models (VLMs) can extract aligned visual and semantic features, making them a promising source of supervision, their direct application is limited. To this end, we propose a Personalized Cross-Modal Emotional Correlation Learning (PCMECL) algorithm that refines VLM-based supervision through two major improvements. First, standard VLMs rely on a single generic prompt for each emotion, failing to capture expressive variations among individuals. PCMECL addresses this limitation by conditioning on individual visual information to learn personalized prompts, thereby establishing more fine-grained visual-semantic correlations. Second, even with personalization, inherent discrepancies persist between the visual and semantic feature distributions. To bridge this modality gap, PCMECL employs feature differencing to correlate the modalities, providing more precisely aligned supervision by matching the change in visual features to the change in semantic features. As a plug-and-play module, PCMECL can be seamlessly integrated into existing SPFEM models. Extensive experiments across various datasets demonstrate the superior efficacy of our algorithm.