UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors

TL;DR

UniVidX is a unified multimodal video generation framework using diffusion priors, enabling versatile, cross-modal, and high-quality video synthesis across various tasks and modalities.

Contribution

It introduces a novel unified framework with key designs like SCM, DGL, and CMSA for flexible, cross-modal video generation, outperforming prior task-specific models.

Findings

Achieves state-of-the-art performance on multiple video generation tasks.

Generalizes well to in-the-wild scenarios with limited training data.

Supports diverse modalities including RGB videos, intrinsic maps, and RGBA layers.

Abstract

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 Masking (SCM) randomly partitions modalities into clean conditions and noisy targets during training, enabling omni-directional conditional generation instead of fixed mappings. Decoupled Gated LoRA (DGL) introduces per-modality LoRAs that are activated when a modality serves as the generation target, preserving the strong priors of the VDM. Cross-Modal Self-Attention (CMSA) shares keys and values across modalities while keeping modality-specific queries, facilitating information exchange and inter-modal alignment. We instantiate UniVidX in two domains: UniVid-Intrinsic, for RGB videos and intrinsic maps including albedo, irradiance, and normal; and UniVid-Alpha, for blended RGB videos and their constituent RGBA layers. Experiments show that both models achieve performance competitive with state-of-the-art methods across distinct tasks and generalize robustly to in-the-wild scenarios, even when trained on fewer than 1,000 videos. Project page: https://houyuanchen111.github.io/UniVidX.github.io/