[Paper] EasyV2V: A High-quality Instruction-based Video Editing Framework

Source: arXiv - 2512.16920v1

Overview

The paper EasyV2V presents a surprisingly simple yet powerful framework for instruction‑based video editing. By cleverly re‑using existing image‑editing experts, leveraging pretrained text‑to‑video models, and introducing a unified mask‑based control scheme, the authors achieve high‑quality, temporally consistent edits that outperform both academic baselines and commercial tools.

Key Contributions

• Data‑centric recipe: Constructs diverse video‑editing pairs from image‑editing experts, single‑frame supervision, and pseudo‑pairs with shared affine motion; mines densely captioned clips to enrich training data.
• Lightweight model design: Shows that a pretrained text‑to‑video diffusion model already contains editing knowledge; fine‑tunes it with a tiny LoRA (Low‑Rank Adaptation) layer and simple sequence concatenation for conditioning.
• Unified spatiotemporal control: Introduces a single mask mechanism that can handle spatial masks, temporal masks, and optional reference images, enabling flexible input modalities (e.g., video + text, video + mask + text, video + mask + reference + text).
• Transition supervision: Trains the model to understand how edits should unfold over time, improving smoothness and consistency across frames.
• State‑of‑the‑art performance: Beats concurrent research and leading commercial video‑editing services on standard benchmarks, while remaining computationally efficient.

Methodology

1. Data Generation

   • Expert composition: Combine off‑the‑shelf image editors (e.g., Stable Diffusion Instruct‑Pix2Pix) with fast inverse models to synthesize before/after image pairs.
   • Lifting to video: Apply the same edit to a single frame and propagate it across a clip using shared affine motion, creating pseudo video pairs without costly manual labeling.
   • Dense caption mining: Crawl video datasets for clips that already have rich textual descriptions, turning them into natural instruction‑video pairs.
   • Transition supervision: Add intermediate frames that gradually morph from source to target, teaching the network the temporal dynamics of edits.

2. Model Architecture

   • Start from a pretrained text‑to‑video diffusion model (e.g., Stable Diffusion Video).
   • Append a LoRA module (a few thousand trainable parameters) to adapt the model to the editing task.
   • Condition the diffusion process by concatenating the source video frames, optional mask, reference image, and the instruction text into a single sequence token stream.

3. Control Mechanism

   • A single binary mask indicates which pixels (and optionally which time steps) should be altered.
   • When a reference image is supplied, the mask also guides where the reference content should be injected.

4. Training

   • Use the constructed video pairs and transition frames.
   • Optimize the LoRA parameters with a modest budget (≈ 1‑2 GPU days on a single A100).

Results & Findings

• Higher semantic alignment: The edited videos match the textual instruction more closely than baselines.
• Better temporal smoothness: Lower FVD indicates fewer flickering artifacts and more coherent motion.
• Human studies: Over 70 % of participants preferred EasyV2V outputs over competing methods.

Qualitative examples (e.g., “turn a daytime street into night while keeping moving cars”) show crisp object changes, consistent lighting shifts, and smooth transitions.

Practical Implications

• Content creation pipelines: Video editors can now script edits with natural language and optional masks, dramatically reducing manual key‑framing.
• Rapid prototyping for AR/VR: Developers can generate variant scenes (e.g., “add snow”) on‑the‑fly without re‑rendering entire assets.
• E‑learning and marketing: Automated video personalization (brand colors, product overlays) becomes feasible with a few lines of instruction.
• Low compute footprint: Since only a LoRA layer is fine‑tuned, companies can adapt the model to domain‑specific vocabularies (e.g., medical imaging) without massive GPU clusters.

Limitations & Future Work

• Scope of edits: The framework excels at global style or object‑level changes but struggles with highly detailed geometry modifications (e.g., precise facial reenactment).
• Mask granularity: While a single mask works for many cases, complex multi‑object edits may require hierarchical masking, which is not yet supported.
• Dataset bias: Training data is derived from existing image‑editing models, potentially inheriting their biases and failure modes.
• Future directions: The authors suggest extending to 3‑D‑aware video editing, integrating depth cues for better occlusion handling, and exploring interactive mask refinement tools for end‑users.

Authors

• Jinjie Mai
• Chaoyang Wang
• Guocheng Gordon Qian
• Willi Menapace
• Sergey Tulyakov
• Bernard Ghanem
• Peter Wonka
• Ashkan Mirzaei

Paper Information

• arXiv ID: 2512.16920v1
• Categories: cs.CV, cs.AI
• Published: December 18, 2025
• PDF: Download PDF