[Paper] In-Context Sync-LoRA for Portrait Video Editing

Source: arXiv - 2512.03013v1

Overview

Portrait video editing has long been a pain point for creators who need to tweak a subject’s look, expression, or surroundings without breaking the natural flow of motion. The paper In-Context Sync‑LoRA for Portrait Video Editing introduces a diffusion‑based pipeline that lets you edit the very first frame of a video and automatically propagate those changes across the entire clip—while keeping every frame perfectly synchronized with the original motion and the subject’s identity.

Key Contributions

• Sync‑LoRA framework: An in‑context Low‑Rank Adaptation (LoRA) that learns to fuse motion cues from the source video with visual edits applied to the first frame.
• Automatic paired‑video generation: A synchronization‑driven filtering pipeline that creates training pairs of videos sharing identical motion trajectories but differing in appearance.
• Compact, highly curated dataset: Only a few hundred tightly synchronized portrait videos are needed to train a model that generalizes to unseen faces and a wide range of edits.
• Frame‑accurate temporal consistency: The method guarantees that each edited frame aligns pixel‑wise with the corresponding source frame’s motion, preserving subtle dynamics like blinking or head turns.
• Broad edit scope: Supports appearance changes (e.g., hair color, makeup), object insertion, background swaps, and expression tweaks—all from a single reference edit.

Methodology

1. Base diffusion model – The authors start with an image‑to‑video diffusion model capable of generating a video sequence from a single image prompt.
2. First‑frame edit – Users edit the first frame using any image‑editing tool (e.g., Photoshop, text‑to‑image prompt). This edited frame becomes the visual target for the whole clip.
3. In‑context LoRA training – A lightweight LoRA module is fine‑tuned on automatically generated video pairs. Each pair shares the exact motion (captured via optical flow) but differs in appearance, teaching the LoRA to “listen” to motion from the source while “speaking” the new visual style from the edited first frame.
4. Synchronization filtering – Before training, the pipeline discards any pair where the motion trajectories drift, ensuring the model only sees perfectly aligned examples.
5. Propagation – At inference, the source video supplies motion embeddings, the edited first frame supplies visual embeddings, and the trained LoRA merges them to synthesize each subsequent frame, guaranteeing frame‑by‑frame alignment.

Results & Findings

• High visual fidelity – Qualitative comparisons show crisp, artifact‑free edits that retain fine details like skin texture and hair strands.
• Temporal coherence – Quantitative metrics (e.g., temporal warping error) are reduced by ~30 % compared to prior diffusion‑based video editors, confirming tighter synchronization.
• Generalization – Even when tested on identities and poses not seen during training, Sync‑LoRA reliably reproduces the intended edits without identity drift.
• Edit versatility – The same model handles diverse tasks—from subtle makeup changes to inserting a virtual object (e.g., a hat) that moves naturally with the head.

Practical Implications

• Content creation pipelines – Video editors can now apply a single image‑level edit (via familiar tools) and automatically get a fully edited video, cutting down manual frame‑by‑frame work.
• Live‑stream graphics – Real‑time avatars or virtual presenters could be re‑skinned on the fly without breaking lip‑sync or head‑movement timing.
• Post‑production for ads & games – Brands can quickly generate multiple variants of a portrait‑centric commercial (different hair colors, accessories) while preserving the original performance capture.
• Developer APIs – The lightweight LoRA means the model can be shipped as a plug‑in for existing diffusion libraries (e.g., Diffusers), enabling easy integration into video‑editing SaaS platforms.

Limitations & Future Work

• Scope limited to portrait videos – The curated dataset focuses on frontal or near‑frontal human heads; extending to full‑body or non‑human subjects may require broader training data.
• Dependence on accurate motion alignment – If the source video contains rapid, erratic motion, the synchronization filter may discard useful pairs, reducing training efficiency.
• Edit granularity tied to first‑frame quality – Very complex multi‑object edits may need higher‑resolution first‑frame inputs or additional conditioning.
• Future directions suggested include scaling the dataset to diverse demographics, exploring multi‑frame conditioning (instead of only the first frame), and optimizing the LoRA for real‑time inference on edge devices.

Authors

• Sagi Polaczek
• Or Patashnik
• Ali Mahdavi‑Amiri
• Daniel Cohen‑Or

Paper Information

• arXiv ID: 2512.03013v1
• Categories: cs.CV, cs.AI, cs.GR
• Published: December 2, 2025
• PDF: Download PDF