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. Critically, it was trained on fewer than 1,000 videos for SIGGRAPH 2026.…