We establish the following novel heterovariant Gaussian Brunn-Minkowski inequality (involving general covariance matrices ) for origin-symmetric convex sets and : whenever , the -geometric mean of . This immediately implies both the dimensional Gaussian Brunn-Minkowski inequality of Eskenazis-Moschidis (by using and optimizing over ) and the strong Gaussian (B)-theorem of Cordero-Erausquin-Fradelizi-Maurey (by using , and diagonal covariances), fusing them into a single unified statement. In fact, the inequality applies to convex sets whose appropriate Gaussian barycenter is at the origin. Furthermore, it reveals a new bilinear version of the strong Gaussian (B)-theorem (involving two convex sets); in particular, for all and : A more general heterovariant version of the (bilinear) (B)-theorem replaces dilation by general linear transformations. A different version yields a heterovariant form of the recently established Gaussian conjugate Rogers-Shephard inequality. By averaging these Gaussian inequalities with respect to the covariance matrices, we obtain dimensional Brunn-Minkowski and conjugate Rogers-Shephard inequalities for Gaussian mixtures. The proofs are based on the Gaussian Forward-Reverse Brascamp-Lieb inequality established in our previous work.

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