UniVidX generates omni-directional video from <1,000 training samples, using diffusion priors with stochastic masking, accepted at SIGGRAPH 2026.

UniVidX, accepted at SIGGRAPH 2026, generates video across RGB, depth, and alpha channels after training on fewer than 1,000 samples. The framework uses diffusion priors with stochastic condition masking to achieve omni-directional generation from a single model.

Key facts

Trained on fewer than 1,000 videos

Accepted at SIGGRAPH 2026 conference

Generates RGB, intrinsic maps, alpha channels

Uses diffusion priors with stochastic masking

No code or benchmark numbers released yet

UniVidX, a unified multimodal framework for versatile video generation, was announced via a tweet from @HuggingPapers. The model enables omni-directional generation across RGB, intrinsic maps, and alpha channels using diffusion priors with stochastic condition masking.…