MAM-REACTOR: ANCHOR-PRESERVING SET-LEVEL ALIGNMENT FOR MULTIPLE APPROPRIATE FACIAL REACTION GENERATION

Offline Multiple Appropriate Facial Reaction Generation (MAFRG) requires more than producing multiple plausible appropriate facial reactions (AFRs): the generated reactions should be both individually appropriate and collectively diverse. Existing offline MAFRG approaches mainly address one-to-many reaction generation through distribution-level modeling or instance-wise supervision. The former models each reaction sequence holistically, capturing global distributional trends but providing weaker fine-grained frame-level alignment, whereas the latter directly supervises individual reactions but does not coordinate multiple predictions as a set. Consequently, neither formulation explicitly organizes the finite output set to complementarily cover multiple valid reactions for the same interaction. We therefore formulate offline MAFRG as a set-level alignment problem. To this end, we propose Mam-Reactor, which combines trajectory-level anchoring with set-level alignment. A deterministic paired-reaction anchor provides a reliable fine-grained appropriateness reference, while expression-identified residual queries and diversity regulation expand it into an expressive candidate set in one forward pass. Balanced prediction–target set alignment then jointly optimizes the correspondence between the complete prediction set and multiple valid same-session reactions, encouraging different candidates to cover complementary valid reaction modes rather than redundantly favoring the same target. A lightweight isolated VA refinement further improves continuous affective trajectories without altering the learned set structure. Experiments on the offline MARS benchmark show that our Mam-Reactor achieved an FRC of 1.0888 and an FRD of 134.81 at an FRDiv of 0.1521. It further generates all ten reactions in a single forward pass, providing approximately $857\times$ faster inference than the 50-step diffusion baseline.