[Paper] From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing

Source: arXiv - 2512.25066v1

Overview

The paper tackles audio‑driven visual dubbing – automatically syncing a video’s lip movements to a new speech track.
Instead of treating the problem as a risky “inpainting” task (where the model must guess missing pixels), the authors turn it into a well‑conditioned video‑to‑video editing problem by first generating perfect training pairs with a diffusion‑based generator. This shift yields far cleaner lip sync, preserves the speaker’s identity, and works robustly on wild, real‑world footage.

Key Contributions

• Self‑bootstrapping pipeline: Uses a Diffusion Transformer (DiT) to synthesize a lip‑altered companion video for every real sample, creating ideal paired data for supervised training.
• Audio‑driven DiT editor: Trains a second DiT model on the generated pairs, allowing it to focus exclusively on precise lip modifications while keeping the full visual context intact.
• Timestep‑adaptive multi‑phase learning: A novel training schedule that separates conflicting editing objectives across diffusion timesteps, stabilizing training and boosting sync fidelity.
• ContextDubBench: A new benchmark covering diverse, challenging dubbing scenarios (different languages, lighting, occlusions, and head poses) for rigorous evaluation.
• State‑of‑the‑art results: Demonstrates superior lip‑sync accuracy, identity preservation, and visual quality compared to prior mask‑inpainting approaches.

Methodology

1. Data Generation (Bootstrapping)

   • Start with a real video clip and its original audio.
   • Feed the clip into a Diffusion Transformer generator conditioned on a synthetic audio track (the target dubbing voice).
   • The generator produces a lip‑altered version of the same clip while keeping everything else (face identity, background, lighting) unchanged.
   • The output and the original clip form a perfectly aligned training pair: source video → target video.

2. Audio‑Driven Editing Model

   • A second DiT‑based editor receives the full source frames (no masks) plus the new audio.
   • Because the input already contains all visual cues, the model only needs to edit the lip region to match the audio, avoiding hallucination of other parts.
   • The editor is trained end‑to‑end on the synthetic pairs, learning a direct mapping from “original video + new speech” → “dubbed video”.

3. Multi‑Phase Diffusion Training

   • Diffusion models denoise from noisy latent representations across timesteps.
   • Early timesteps require coarse structural changes, later timesteps fine‑grained texture edits.
   • The authors introduce a timestep‑adaptive schedule that applies different loss weights and learning rates at each phase, disentangling the need for global consistency (identity, pose) from precise lip motion, which stabilizes training.

4. Evaluation (ContextDubBench)

   • The benchmark contains 1,200 clips spanning 12 real‑world dubbing challenges (e.g., extreme head turns, low‑light, multiple speakers).
   • Metrics include Lip‑Sync Error (LSE‑C), Identity Similarity (ArcFace), and perceptual video quality (LPIPS, FVD).

Results & Findings

• Lip synchronization improves by >55 % on average.
• Identity drift is virtually eliminated; the edited faces retain the original person’s features even under extreme pose changes.
• Robustness: The model maintains quality on low‑resolution, noisy, and multi‑person scenes where mask‑inpainting typically fails.
• Ablation studies confirm that (i) the synthetic paired data, (ii) full‑frame conditioning, and (iii) the multi‑phase schedule each contribute significantly to the final performance boost.

Practical Implications

• Content Localization: Studios can dub movies, TV shows, or short videos with far less manual retouching, preserving actors’ likenesses and avoiding uncanny artifacts.
• Real‑Time Applications: Because the editor works on full frames rather than masked patches, it can be integrated into streaming pipelines where low latency is crucial (e.g., live translation of webinars).
• AR/VR Avatars: Developers building conversational avatars can leverage the framework to sync synthetic speech with a user’s facial video, ensuring consistent identity and high visual fidelity.
• Accessibility Tools: Automatic dubbing for the hearing‑impaired (e.g., sign‑language overlays) can be paired with this technology to keep the visual narrative coherent.
• Dataset Generation: The self‑bootstrapping approach can be repurposed to create paired training data for other video editing tasks (e.g., expression transfer, style adaptation) without costly manual annotation.

Limitations & Future Work

• Synthetic Training Gap: Although the generated pairs are visually aligned, they are still synthetic; subtle domain gaps may appear when dubbing extremely high‑resolution cinema footage.
• Audio Quality Dependency: The editor assumes a clean, time‑aligned audio track; noisy or misaligned speech can degrade sync accuracy.
• Computational Cost: Training two diffusion transformers (generator + editor) demands substantial GPU resources, which may limit adoption for smaller teams.
• Future Directions:
  • Investigate domain adaptation techniques to bridge the synthetic‑real gap for 4K content.
  • Extend the framework to multi‑speaker dubbing where multiple faces need coordinated lip edits.
  • Explore lightweight inference variants (e.g., knowledge distillation) for on‑device real‑time dubbing.

Bottom line: By turning visual dubbing into a well‑conditioned video editing problem and using diffusion models both to create perfect training pairs and to perform the edit, the authors deliver a system that dramatically improves lip sync, identity preservation, and robustness—opening the door to practical, high‑quality dubbing solutions for developers and media creators alike.

Authors

• Xu He
• Haoxian Zhang
• Hejia Chen
• Changyuan Zheng
• Liyang Chen
• Songlin Tang
• Jiehui Huang
• Xiaoqiang Liu
• Pengfei Wan
• Zhiyong Wu

Paper Information

• arXiv ID: 2512.25066v1
• Categories: cs.CV
• Published: December 31, 2025
• PDF: Download PDF