Multimodal Generation
Overview
Multimodal generation studies models that create outputs such as images, video, speech, motion, or scientific signals from text, media, or combinations of them. Common approaches include diffusion and flow-matching decoders, autoregressive or discrete-token models, shared latent spaces, and systems that connect vision-language models to modality-specific generators; planning and reinforcement learning can add control for complex or interleaved outputs. A major direction is building models that both understand and generate, with benchmarks measuring prompt adherence, fidelity, temporal and cross-modal coherence, and task-specific validity. An open challenge is enabling shared models to turn understanding into faithful, controllable generation without modality imbalance or loss of specialized quality, especially over long sequences and varied real-world inputs.
Latest papers
243 papers29 papers in the last four weeks, up 38% on the four weeks before. 0.2% of all new papers.