Lightweight Multiple Appropriate Facial Reaction Generation: Pruning-Regeneration and Correction Hybrid Precision Quantization

Advances in artificial intelligence have made Multiple Appropriate Facial Reaction Generation (MAFRG) increasingly important in human-computer interaction. However, existing models face practical deployment challenges due to complex structures and high computational costs. This paper proposes a lightweight framework for generating appropriate multimodal facial reactions, comprising two complementary methods targeting structural sparsity and numerical precision, respectively. DyPR-Net dynamically adjusts its sparse structure via adaptive thresholding and a weight regeneration mechanism to restore critical connections, balancing compression efficiency with facial reaction fidelity. GOMD-Net addresses semantic drift and degraded expression quality under low-bit quantization by adaptively allocating bit widths across layers based on inter-layer orthogonality. It further employs a Distribution-Skew-Correction (DSC) strategy combined with a global optimization objective to faithfully reproduce subtle and diverse emotional expressions under extremely low bit-width constraints. Extensive experiments across multiple mainstream architectures on the NoXi and RECOLA datasets demonstrate the effectiveness and potential generalizability of the proposed framework, reducing model size, FLOPs in the tested environment, thereby offering a promising basis toward the lightweight deployment of MAFRG models.