FaVChat: Unlocking Fine-Grained Facial Video Understanding with Multimodal Large Language Models

• URL: http://arxiv.org/abs/2503.09158v2
• Date: Thu, 13 Mar 2025 10:45:03 GMT
• Title: FaVChat: Unlocking Fine-Grained Facial Video Understanding with Multimodal Large Language Models
• Authors: Fufangchen Zhao, Ming Li, Linrui Xu, Wenhao Jiang, Jian Gao, Danfeng Yan,
• Abstract summary: FaVChat is the first VMLLM specifically designed for fine-grained facial video understanding.<n>We construct a large-scale facial video dataset comprising over 60k videos, with the majority annotated with 83 fine-grained facial attributes.<n>We employ a progressive training paradigm, transitioning from video summarization to a high-quality subset of video QA, gradually increasing task complexity to enhance the model's fine-grained visual perception.
• Score: 12.029771909598647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video-based multimodal large language models (VMLLMs) have demonstrated remarkable potential in cross-modal video understanding. However, their abilities in fine-grained face comprehension remain largely underexplored. Given its pivotal role in human-centric intelligence, developing VMLLMs for facial understanding holds a fundamental problem. To address this gap, we propose FaVChat, the first VMLLM specifically designed for fine-grained facial video understanding. To facilitate its training, we construct a large-scale facial video dataset comprising over 60k videos, with the majority annotated with 83 fine-grained facial attributes. These attributes are incorporated to enrich GPT-4o-generated captions, yielding 60k high-quality video-summary pairs and an additional 170k fine-grained question-answering (QA) pairs. To effectively capture rich facial clues, we propose a hybrid model architecture composed of a general visual encoder, a dedicated facial encoder, and a mixture-of-experts-enhanced adapter for adaptive fusion of multi-source visual features. To mitigate information loss during feature transformation, we extract multi-granularity representations from the facial encoder and integrate them into the subsequent LLM. This design enhances the model's ability to comprehend and respond to questions involving diverse levels of visual details. We employ a progressive training paradigm, transitioning from video summarization to a high-quality subset of video QA, gradually increasing task complexity to enhance the model's fine-grained visual perception. We conduct extensive zero-shot evaluation on a couple of public benchmarks, demonstrating that FaVChat consistently surpasses existing VMLLMs across multiple tasks.

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