Source-linked AI summary
ThinkV2V: Unleashing the Reasoning Capability of MLLMs for Instruction-Guided Video Editing
Donghao Zhou, Haoyang He, Fan Zhang, Hao Yang, Guisheng Liu, Xin Gao, Zhongwei Wan, Xingyuan Bu, Jie Wang, Qiangpeng Yang, Shilei Wen, Chi-Wing Fu, Pheng-Ann Heng
TL;DR
Implicit video-editing instructions can require causal or semantic reasoning, while existing methods mainly use MLLMs as semantic encoders. ThinkV2V activates MLLM thinking before generation through an MLLM-to-DiT framework and reasoning-focused training and inference; it reports state-of-the-art results across complex and standard editing scenarios, with a 5B-scale DiT surpassing larger 10B-scale baselines.
Problem
Existing video editors mainly use MLLMs as semantic encoders and often struggle with implicit instructions whose intent requires causal or semantic reasoning.
Source: §1 · p. 1Method
ThinkV2V activates MLLM thinking before video generation and combines an MLLM-to-DiT architecture with curriculum training and iterative inference-time prompt refinement.
Source: §1 · p. 2Results
Takeaways & Limitations
The results suggest that explicitly incorporating reasoning is a promising direction for improving real-world usability in instruction-guided video editing.
Source: §5 · p. 8The study does not independently evaluate thinking-content correctness, and its dataset and benchmark have limited coverage of open-domain, longer-horizon, and rarer complex edits.
Source: Appendix C · p. 12
Abstract
from arXiv · showhide
Instruction-guided video editing has made significant progress, yet existing methods use multimodal large language models (MLLMs) primarily as semantic encoders, so they often fall short in working with implicit edits that require causal or semantic reasoning. To bridge this fundamental gap in video editing, we propose ThinkV2V, a reasoning-driven framework for complex instruction-guided video editing, explicitly activating MLLM thinking before visual generation. At its core, ThinkV2V builds on a practical MLLM-to-DiT architecture to turn explicit thinking over the source video and instruction into refined conditioning signals for video editing. Further, we equip it with a dedicated training and inference recipe, combining Progressive Curriculum Training, which gradually cultivates the model from basic editing to reasoning-intensive cases, with Inference-Time Thinking Scaling, which iteratively refines candidate prompts and selects the most reliable one, to better elicit reasoning in challenging editing scenarios. We also curate the ThinkV2V-150K dataset and introduce ThinkV2V-Bench to support training and evaluation of video editing with implicit intent and causal reasoning. Experimental results demonstrate the state-of-the-art performance of ThinkV2V on both complex and standard editing scenarios, in which our 5B-scale DiT model substantially outperforms larger 10B-scale baselines.
1 INTRODUCTION
Implicit video-editing instructions often require causal or semantic reasoning, but existing systems mainly use MLLMs as semantic encoders. ThinkV2V activates MLLM thinking before generation, combining an MLLM-to-DiT architecture and a dedicated training-and-inference recipe; it reports strong results across complex and standard editing.
1 INTRODUCTION
- Existing editors often struggle with implicit instructions because they use MLLMs mainly as semantic encoders rather than explicitly reasoning about intent.Surface-keyword focus can miss objectives involving temporal dynamics, object relations, or logical causality.Source: §1 · p. 1
- Progressive Curriculum Training moves from basic editing toward reasoning-intensive cases, while Inference-Time Thinking Scaling refines candidate prompts and selects the most reliable.The selected prompt is used before the DiT process.Source: §1 · p. 2
- ThinkV2V achieves state-of-the-art performance on complex and standard editing scenarios, with its 5B-scale DiT surpassing several substantially larger 10B-scale baselines.The overview figure presents quantitative results across both editing scenarios.Source: §1 · p. 2, Figure 1 · p. 2
- ThinkV2V proposes a practical MLLM-to-DiT architecture that uses explicit thinking to understand and plan edits before generation.The MLLM takes the source video and instruction, produces a refined prompt, and supplies hidden features refined by a connector and injected into the DiT.Source: §1 · pp. 1–2
- ThinkV2V-150K and ThinkV2V-Bench provide data and evaluation for complex editing involving implicit intent and causal reasoning.The paper identifies a gap in datasets and benchmarks that emphasize these capabilities.Source: §1 · pp. 1–2
2 RELATED WORK
Prior work advances instruction-guided editing through feature-fusion strategies and applies explicit reasoning to multimodal understanding and visual generation. The paper identifies limited support for reasoning about implicit intent in video editing.
Instruction-guided Image and Video Editing
- Video editors use sequence concatenation [8], [10], [13], channel concatenation, [6], or element-wise addition [20], yet provide limited support for reasoning over implicit intent.Other strategies include dual Instruction and Reference Guidance [21] and decoupling semantic anchoring from motion alignment [22].Source: §2 · p. 3
Reasoning-Enhanced Visual Generation and Editing
- LLaVA-CoT generates structured visual thought, while BLINK-Twice [24] emphasizes fine-grained analytical reasoning for multimodal understanding..Source: §2 · p. 3
- Reasoning has been integrated into image and video generation, [26], [27], but its systematic extension to video editing remains underexplored.The paper says prior instruction-guided editors remain focused on perception-level manipulations and fail on spatial-temporal reasoning tasks; self-reflective editing [28] lacks a mechanism to decouple MLLM cognition from the generator.Source: §2 · p. 3
3 METHODOLOGY
ThinkV2V combines explicit MLLM reasoning with a connector and multi-condition DiT, supported by staged training, test-time candidate selection, and purpose-built data and benchmark construction.
3.1 Model Architecture
- The MLLM reasons over the source video and instruction, producing a refined prompt and answer-token hidden states that serve as semantic features for editing.The method treats the MLLM as an explicit reasoner rather than a static semantic encoder.Source: §3.1 · p. 3
- A learnable-query connector compresses and aligns MLLM features for the DiT, which also receives original-instruction embeddings and source-video latents.The original instruction is concatenated token-wise with connector features, while source-video VAE features are concatenated channel-wise with noisy latents.Source: §3.1 · pp. 3–4
3.2 Training and Inference Recipe
- Progressive Curriculum Training advances from basic alignment through high-resolution adaptation to reasoning-intensive tuning, moving from OpenVE-HQ-1M to ThinkV2V-150K.The MLLM remains frozen while the connector and DiT are optimized.Source: §3.2 · pp. 4–5
- Inference-Time Thinking Scaling iteratively refines prompts, then uses best-of-8 MLLM selection to choose a candidate matching the original intent and source video.The selected candidate's cached hidden states are passed to the connector and DiT.Source: §3.2 · p. 5
3.3 Dataset and Benchmark Construction
- ThinkV2V-150K contains 150K reasoning-oriented video pairs, created by synthesizing complex instructions for samples selected from five editing categories.The source samples are filtered and scored before selecting 20K to 40K per category for instruction synthesis.Source: §3.3 · p. 5
- ThinkV2V-Bench rewrites direct instructions into reasoning-oriented ones, yielding 308 pairs across the same five categories for evaluating implicit-intent editing.Together, the dataset and benchmark form a data-and-evaluation loop for systematic training and validation.Source: §3.3 · p. 5
4 EXPERIMENTS
ThinkV2V leads on both complex and standard editing benchmarks, and qualitative comparisons show more instruction-aligned edits. Ablations examine its architecture, staged training, and inference-time scaling.
4.2 Quantitative comparison
- ThinkV2V achieves the best overall score under both judges on ThinkV2V-Bench and OpenVE-Bench, despite using a 5B-scale DiT backbone.The caption identifies Table 1 as the comparison across both benchmarks and judge models.Source: Table 1 · p. 6, §4.2 · pp. 6–7
4.1 Setup
- Experiments compare methods on one 80GB GPU, using each baseline's best resolution and frame-count configuration that avoids out-of-memory errors.Training uses a 32-GPU cluster and proceeds through three stages, from 480p OpenVE-HQ-1M training to 720p training on ThinkV2V-150K.Source: §4.1 · pp. 5–6
4.3 Qualitative comparison
- Across five reasoning-oriented edit types, ThinkV2V more reliably aligns edits with full instructions while preserving unedited content and temporal consistency.Examples include global style, background change, local change, local removal, and local addition; baselines sometimes misplace additions or alter the wrong target.Source: §4.3 · p. 7, Figure 4 · p. 7
4.4 Ablation Studies and Analysis
- 2.68 overall score with Answer Features exceeds 2.63 with Thinking + All Features and 2.51 without Thinking.The authors suggest final-answer hidden states reduce redundant thinking tokens and provide a more execution-oriented condition.Source: §4.4 · p. 7, Table 2 · p. 8
- 2.68 overall score for Simple-to-Complex training exceeds the scores for Complex Only (2.10), Complex-to-Simple (2.41), Mixed Simple+Complex (2.47), and Simple Only (2.52).The comparison favors a structured transition from basic editing to reasoning-intensive editing over single-type, reversed-order, or unstaged training.Source: Table 2 · p. 8, §4.4 · p. 8
- 2.72 overall score with Selection after Serial Refinement is the best result in this group, versus 2.67 with Serial Refinement alone.The authors note that refinement alone may amplify local ambiguities without selection.Source: Table 2 · p. 8, §4.4 · p. 8
5 CONCLUSION
The conclusion presents ThinkV2V as a reasoning-driven approach to bridging implicit language intent and executable video edits. Results favor its use on reasoning-intensive scenarios while remaining competitive on standard tasks.
5 CONCLUSION
- ThinkV2V addresses the gap between implicit language intent and executable editing through a reasoning-driven video-editing framework.The paper explores architecture, training and inference, and dataset and benchmark construction.Source: §5 · p. 8
- ThinkV2V outperforms existing methods on reasoning-intensive editing and remains competitive on standard video-editing tasks.Source: §5 · p. 8
- The authors identify explicitly incorporating reasoning as a promising direction for improving real-world usability and propose exploring more efficient thinking-involved frameworks.Source: §5 · p. 8
10 APPENDICES
The appendices cover dataset and benchmark construction, additional qualitative comparisons, limitations and future directions, and societal impacts.
Appendix A
- Appendix A details construction of ThinkV2V-150K and ThinkV2V-Bench.Source: §10 · p. 10
Appendix B
- Appendix B presents additional ThinkV2V-Bench comparisons and challenging cases for ThinkV2V.Source: §10 · p. 10
Appendix C
- Appendix C discusses current limitations and future directions for ThinkV2V.Source: §10 · p. 10
Appendix D
- Appendix D presents ThinkV2V's broader societal impacts.Source: §10 · p. 10
A.1 ThinkV2V-150K
ThinkV2V-150K is constructed by filtering source video edits for quality and reasoning suitability, then rewriting direct instructions as indirect, inference-heavy requests grounded in visual context.
A.1 ThinkV2V-150K
- The dataset retains Global Style, Background Change, Local Remove, Local Add, and Local Change, excluding camera, subtitle, and creative edits.The selected categories span scene- and object-level edits grounded in the source video.Source: §A.1 · p. 10
- Quality filtering combines Gemini-2.5-Flash instruction-alignment and plausibility scores with CLIP-F and Temporal Flickering signals.The top 50% by Gemini score form an initial candidate pool; subsequent multi-criteria filtering yields OpenVE-HQ-1M as the source pool.Source: §A.1 · p. 10
- ThinkV2V-150K contains 150K video pairs selected for rewriting instructions into indirect, reasoning-based descriptions.The pipeline retains 20K to 40K samples per category after suitability filtering.Source: §A.1 · p. 10
- Gemini-2.5-Pro rewrites direct instructions using the source and edited videos’ first-frame context, replacing direct target names with indirect references while preserving the intended edit.The rewrite instructions favor natural user-like requests and remove generic quality-control phrases.Source: §A.1 · pp. 10–11
A.2 ThinkV2V-Bench
ThinkV2V-Bench targets a gap in standard editing benchmarks: direct target naming can permit shallow matching rather than testing inference from contextual or relational cues. It rewrites instructions while preserving intended edits to create a focused testbed for reasoning-driven editing.
A.2 ThinkV2V-Bench
- Standard benchmarks often name the target or transformation directly, so they may not test inference from contextual or relational descriptions.ThinkV2V-Bench is introduced to evaluate implicit-intent understanding more directly.Source: §A.2 · p. 11
- The benchmark uses the same five reasoning-suitable categories as ThinkV2V-150K and excludes camera, subtitle, and creative edits.This alignment keeps the task definition coherent across training and evaluation and focuses results on the intended editing regime.Source: §A.2 · p. 11
- ThinkV2V-Bench contains 308 video-instruction pairs across five categories, with indirect instructions rewritten using visual context and intended edits preserved.Gemini-3.1-Pro performs the rewriting, which shifts target identification toward context, relations, position, action, and function.Source: §A.2 · p. 11
A.3 Scope and Summary
ThinkV2V-150K and ThinkV2V-Bench pair large-scale reasoning-oriented supervision with a controlled test of implicit-intent execution.
A.3 Scope and Summary
- ThinkV2V-150K and ThinkV2V-Bench form a data-and-evaluation loop pairing reasoning-oriented training supervision with a controlled assessment of implicit-intent editing.The pairing is intended to validate whether editing relies on reasoning over language, scene context, and causal structure as well as visual generation quality.Source: §A.3 · p. 12
- ThinkV2V-150K shifts training toward realistic, reasoning-oriented user requests.The passage describes the dataset’s key statistics in Figure 3.Source: §A.3 · p. 12
- ThinkV2V-Bench provides a controlled benchmark for assessing whether training translates into better execution of implicit intent.The passage also presents a representative benchmark example in Figure 3.Source: §A.3 · p. 12
APPENDIX B MORE QUALITATIVE RESULTS
Additional qualitative comparisons report that ThinkV2V handles indirect targets and demanding scene constraints, while existing methods can miss the intended edit or alter unintended regions.
APPENDIX B MORE QUALITATIVE RESULTS
- Across complex real-world instructions, ThinkV2V’s qualitative results contrast with existing methods’ wrong-target edits, unintended local drift, and surface-keyword matching.The comparisons cover Global Style, Background Change, Local Remove, Local Add, and Local Change scenarios.Source: Appendix B · p. 12
- In a Local Remove case, the instruction identifies a dune buggy through visual attributes while requiring other vehicles’ positions and slow motion to remain intact.The edit also requires seamless background reconstruction.Source: Appendix B · p. 12
- In a Global Style case, the video is transformed to a watercolor-like style while preserving scene elements, their spatial relations, and the train’s motion trajectory.The described elements include the locomotive, traffic light, power poles, vegetation, and mountains.Source: Appendix B · p. 12
- The two challenging cases illustrate ThinkV2V resolving indirect target references while maintaining scene structure, motion continuity, and global consistency.The passage presents these as qualitative support for performance on complex real-world instructions.Source: Appendix B · p. 12
APPENDIX C LIMITATIONS AND FUTURE WORK
The study identifies computation and latency costs, the lack of independent evaluation for reasoning traces, and limited dataset and benchmark coverage as current limitations. It proposes more efficient reasoning and broader data coverage, and discusses both potential benefits and misuse risks of video editing.
APPENDIX C LIMITATIONS AND FUTURE WORK
- Explicit thinking adds computation and latency, reasoning-trace correctness is not independently evaluated, and dataset coverage remains limited for open-domain, longer-horizon, and rare complex edits.The authors note that rigorous quantitative protocols for evaluating semantic reasoning traces are unavailable.Source: Appendix C · p. 12
- The authors propose more efficient reasoning-involved frameworks, including latent reasoning, and broader training data and benchmarks covering richer patterns, scenarios, and video domains.These directions may support visual content creation systems with stronger instruction understanding, planning, and execution capabilities.Source: Appendix C · p. 12
APPENDIX D IMPACT STATEMENT
- The paper identifies reduced manual trial and error in complex editing workflows as a potential benefit, while noting risks of misleading or impersonation content.The authors also connect potential misuse to risks involving bias, privacy, and authorization.Source: Appendix D · pp. 12–13
- The planned responsible release includes usage terms, restrictions on high-risk use cases, and documentation of licenses and intended use.Source: Appendix D · p. 13
- Figure 6 compares methods on additional ThinkV2V-Bench examples and reports more intention-consistent edits from ThinkV2V.Source: Figure 6 · p. 14
- Figures 7–8 illustrate challenging cases involving implicit target identification, fine-grained scene understanding, and strong consistency requirements.Source: Figure 7 · p. 15, Figure 8 · p. 16