[Paper] ReViSE: Towards Reason-Informed Video Editing in Unified Models with Self-Reflective Learning

Source: arXiv - 2512.09924v1

Overview

The paper introduces ReViSE, a unified video‑editing model that can reason about physical plausibility and causal dynamics before it changes a clip. By coupling a vision‑language reasoning module with the generator, the system can self‑check whether its edits actually satisfy the user’s instruction—something that prior “unified” video models struggled with. To make this possible, the authors also release RVE‑Bench, a new benchmark that evaluates both reasoning‑aware editing and in‑context video generation.

Key Contributions

• Reason‑Informed Video Editing (RVE) task: formalizes editing that must respect physical and causal reasoning (e.g., “make the ball bounce higher without breaking the floor”).
• RVE‑Bench: a two‑part benchmark (Reasoning‑Informed Editing + In‑Context Generation) covering diverse real‑world scenarios and reasoning dimensions.
• ReViSE architecture: a self‑reflective framework that integrates a Vision‑Language Model (VLM) as an internal critic, providing differentiable feedback to the video generator.
• Self‑Reflective Reasoning (SRF) loss: trains the generator to align its output with the VLM’s logical assessment, closing the gap between understanding and editing.
• Empirical gains: ReViSE lifts the overall score on the reasoning‑informed editing subset by 32 % versus the strongest baselines, while also improving visual fidelity.

Methodology

1. Unified backbone – ReViSE builds on a transformer‑based video‑generation model that can accept text prompts and produce frames autoregressively.
2. Internal VLM critic – A pre‑trained vision‑language model (e.g., CLIP‑Video) processes the edited video together with the original instruction and outputs a “reasonability score.”
3. Self‑reflective loop – During training, the generator’s output is fed to the VLM; the gradient of the VLM’s score is back‑propagated through the generator via a differentiable reasoning loss (SRF). This nudges the generator to produce edits that the VLM deems logically consistent.
4. Joint generation & evaluation – The same architecture can also be used for in‑context video generation, where the VLM checks whether a newly generated clip follows a multi‑step narrative.
5. Benchmarking – RVE‑Bench supplies paired “before/after” videos, textual instructions, and ground‑truth reasoning annotations (e.g., physical constraints, causal chains). Evaluation combines standard video quality metrics (FID, CLIP‑Score) with a newly proposed Reasoning Accuracy metric derived from the VLM’s judgments.

Results & Findings

• 32 % boost in the overall reasoning‑informed editing score demonstrates that the self‑reflective loop effectively aligns generation with logical constraints.
• Visual quality improves simultaneously, indicating that the reasoning feedback does not sacrifice fidelity.
• Ablation studies show that removing the SRF loss drops reasoning accuracy by ~15 %, confirming its central role.

Practical Implications

• Content creation pipelines – Video editors can now ask a single model to “make the car accelerate faster while keeping the road surface intact,” and the model will respect physics without manual post‑editing.
• Simulation & training data generation – Autonomous‑driving or robotics simulators can generate scenario variations that remain physically plausible, reducing the need for hand‑crafted rule sets.
• Interactive AI assistants – Chat‑based tools that manipulate video (e.g., “show me a cup spill without breaking the table”) can rely on a single unified model rather than chaining separate reasoning and synthesis modules.
• Safety‑critical domains – In AR/VR or medical video augmentation, ensuring that edits obey causal constraints can prevent misleading visualizations.

Limitations & Future Work

• Reliance on VLM quality – The self‑reflective feedback is only as good as the underlying vision‑language model; biases or blind spots in the VLM propagate to the generator.
• Scalability to long videos – Current experiments focus on clips ≤ 5 seconds; extending the approach to minute‑scale footage will require more efficient temporal modeling.
• Reasoning granularity – The benchmark covers a predefined set of physical and causal rules; real‑world editing may involve richer, domain‑specific knowledge (e.g., fluid dynamics) that the current VLM cannot assess.
• Future directions proposed by the authors include: integrating multimodal reasoning (audio, depth), training the VLM jointly with the generator for tighter alignment, and expanding RVE‑Bench with user‑generated “wild‑type” editing tasks.

Authors

• Xinyu Liu
• Hangjie Yuan
• Yujie Wei
• Jiazheng Xing
• Yujin Han
• Jiahao Pan
• Yanbiao Ma
• Chi‑Min Chan
• Kang Zhao
• Shiwei Zhang
• Wenhan Luo
• Yike Guo

Paper Information

• arXiv ID: 2512.09924v1
• Categories: cs.CV
• Published: December 10, 2025
• PDF: Download PDF