UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
Recent progress has shown that video diffusion models (VDMs) can be repurposed for diverse multimodal graphics tasks. However, existing methods often train separate models for each problem setting, which fixes the input-output mapping and limits the modeling of correlations across modalities. We present UniVidX, a unified multimodal framework that leverages VDM priors for versatile video generation. UniVidX formulates pixel-aligned tasks as conditional generation in a shared multimodal space, adapts to modality-specific distributions while preserving the backbone's native priors, and promotes cross-modal consistency during synthesis. It is built on three key designs. Stochastic Condition ...
| Authors | Houyuan Chen, Hong Li, Xianghao Kong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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