From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting

TL;DR - D2DF is a framework that distills a multi-step diffusion teacher into a one-step video generation model for object removal, cutting per-video denoising to ~1 second while claiming state-of-the-art quality.

• Trains a teacher to refine coarse "drafts" (low-quality removal results) into high-fidelity videos over multiple steps, then uses Prior-Privileged Consistency Distillation (PPCD) to transfer this into a one-step student conditioned on the draft.
• Adds a Self-Guided Fast Planting (SGFP) module, built on a Temporal Masked Transformer, that generates scene-consistent pseudo-drafts in latent space to remove draft dependency entirely.
• Positions itself against artifact-prone optical-flow/attention methods and slow multi-step diffusion, reporting both draft-conditioned and draft-free variants beat prior work on multiple quality metrics.
• Efficiency is the headline claim: single-video denoising in about 1 second (specific benchmark numbers not provided in the abstract).