Unified Video Dense Prediction from Disjoint Data

TL;DR - UniD is a unified video model that predicts eight dense scene properties using disjoint, task-specific datasets. It avoids costly pseudo-labeling by distilling expert supervision into a diffusion-pretrained shared backbone.

• Predicts depth, normals, semantics, boundaries, human parts, albedo, shading, and materials.
• Lightweight task projectors transfer knowledge from per-task experts without overlapping annotations.
• Pretrained diffusion priors help bridge domain gaps and generalize to unseen scene-task combinations.
• Achieves competitive specialist-level performance with improved temporal and cross-task consistency.