A Survey of Generative Categories and Techniques in Multimodal Generative Models

arXiv:2506.10016v4 Announce Type: replace-cross Abstract: Multimodal generative models (MGMs) have rapidly expanded beyond text generation to encompass images, music, video, human motion, and 3D objects. This survey categorises six primary text-conditioned generative modalities and examines how four techniques, namely self-supervised learning, mixture of experts, reinforcement learning from human feedback, and chain-of-thought prompting, are adapted across them, distinguishing shared mechanisms from experimentally supported benefits. We organise evaluation around faithfulness, compositionality

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