[Paper] EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers

Source: arXiv - 2601.22127v1

Overview

EditYourself tackles a long‑standing pain point for video creators: how to change the spoken words in an existing talking‑head clip without re‑shooting or sacrificing visual quality. By marrying diffusion‑based video generation with audio conditioning and a transformer backbone, the authors deliver a system that can add, delete, or retime speech while keeping the original motion, identity, and lip‑sync intact.

Key Contributions

• Audio‑driven video‑to‑video editing: Extends a general‑purpose video diffusion model (DiT) to accept raw audio as a conditioning signal, enabling transcript‑level edits of existing footage.
• Region‑aware spatiotemporal inpainting: Introduces edit masks that focus the diffusion process on the mouth and facial regions, preserving untouched areas and ensuring temporal coherence.
• Edit‑focused training regime: Augments the diffusion training set with synthetic “edit” scenarios (speech insertion, deletion, and retiming) so the model learns to handle realistic post‑production workflows.
• Long‑duration identity consistency: Demonstrates stable speaker identity and motion over clips up to several seconds, a notable improvement over prior short‑clip generators.
• Open‑source implementation & API prototype: Provides a ready‑to‑use Python package and a lightweight REST endpoint, lowering the barrier for integration into existing pipelines.

Methodology

1. Base Model – DiT (Diffusion Transformer)

   • A transformer‑based diffusion model that predicts video frames in a latent space, trained on large‑scale talking‑head datasets.

2. Audio Conditioning

   • Raw waveform is passed through a pretrained audio encoder (e.g., wav2vec‑2.0) to produce a time‑aligned embedding.
   • The embedding is injected into every diffusion timestep via cross‑attention, guiding the visual synthesis toward the desired phonemes.

3. Edit Mask Generation

   • Users supply a transcript edit (e.g., “replace ‘hello’ with ‘welcome’”).
   • An automatic alignment step maps the new transcript to timestamps, producing a binary mask that covers the mouth region for the affected frames.

4. Spatiotemporal Inpainting

   • The diffusion process runs only on masked regions while the rest of the video is kept as a conditioning signal.
   • A temporal attention window ensures that generated frames blend smoothly with surrounding context.

5. Training Augmentation

   • Synthetic edits are created on‑the‑fly (randomly inserting, deleting, or stretching audio) and the model is trained to reconstruct the resulting video, teaching it to handle real‑world editing operations.

Results & Findings

• Lip‑sync improves by ~57 % thanks to the audio‑conditioned cross‑attention.
• Identity drift over 5‑second clips drops to near‑imperceptible levels, enabling long edits without the “uncanny” feel.
• Qualitative examples show seamless insertion of new sentences, removal of filler words, and smooth retiming of pauses, all while preserving background lighting and head pose.

Practical Implications

• Post‑production pipelines: Editors can now fix script errors, localize content, or create multilingual versions without costly re‑shoots.

• Live‑stream augmentation: Real‑time audio feeds could be used to correct mispronunciations or censor profanity on‑the‑fly.

• E‑learning & corporate training: Update outdated narration in recorded lectures while keeping the original presenter’s presence.

• Accessibility tools: Generate sign‑language overlays or lip‑readable videos by swapping audio tracks for different languages.

• SDK integration: The provided Python package can be dropped into existing video‑processing stacks (e.g., FFmpeg‑based workflows) with a single API call:

  edit_video(input.mp4, new_transcript, audio.wav)

Limitations & Future Work

• Domain specificity: The model is trained primarily on frontal, well‑lit talking‑head datasets; performance degrades on extreme angles, heavy occlusions, or low‑resolution footage.
• Audio quality dependence: Noisy or heavily reverberated audio reduces lip‑sync accuracy; future work will explore robust audio encoders and denoising front‑ends.
• Edit length: While 5‑second edits are stable, longer insertions (>10 s) still show slight identity drift, suggesting a need for hierarchical temporal modeling.
• Real‑time constraints: Current inference runs at ~2 fps on a single A100 GPU; optimizing the diffusion schedule or leveraging distillation could bring the system closer to live‑editing speeds.

EditYourself marks a concrete step toward making generative video models practical tools for everyday video editing, opening the door for more flexible, AI‑augmented post‑production workflows.

Authors

• John Flynn
• Wolfgang Paier
• Dimitar Dinev
• Sam Nhut Nguyen
• Hayk Poghosyan
• Manuel Toribio
• Sandipan Banerjee
• Guy Gafni

Paper Information

• arXiv ID: 2601.22127v1
• Categories: cs.CV, cs.GR, cs.LG, cs.MM
• Published: January 29, 2026
• PDF: Download PDF