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arxiv: 2606.24225 · v1 · pith:O2ISWF5Unew · submitted 2026-06-23 · 💻 cs.CV

Geometry-Instructed Video Editing

Chirui Chang , Xiaoyang Lyu , Yi-Hua Huang , Haoru Tan , Shizhen Zhao , Yikang Ding , Jianmin Bao , Xin Tao
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Pengfei Wan Xiaojuan Qi
This is my paper

Pith reviewed 2026-06-26 00:41 UTC · model grok-4.3

classification 💻 cs.CV
keywords video editinggeometric editsobject manipulationdepth boxorientation boxsynthetic datagenerative modelscomputer vision
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The pith

GIVE uses depth-box and orientation-box streams to specify 3D object state changes for reliable video geometric edits.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GIVE as a framework for performing object-level geometric edits such as translating, rotating, scaling, duplicating or removing objects in videos. It tackles the difficulty of specifying unambiguous 3D state changes across time and viewpoints while updating related effects like shadows consistently. Two geometry streams, depth-box for placement and orientation-box for cues, provide the specification. A graphics pipeline creates training data with before and after pairs. Results show the method works across edit types and transfers to real videos.

Core claim

GIVE represents edits through a unified object-state formulation. Two video-aligned geometry streams describe the target object before and after editing: a depth-box encoding coarse 3D placement and extent, and an orientation-box providing an appearance-agnostic orientation cue. A scalable graphics-engine pipeline executes object-level edit programs and renders controlled before/after pairs to provide paired supervision.

What carries the argument

Unified object-state formulation using depth-box and orientation-box geometry streams for specifying pre and post edit states.

Load-bearing premise

The graphics-engine pipeline generates paired videos that isolate the intended geometric edit and keep secondary effects consistent, and that this synthetic data transfers to real videos.

What would settle it

Editing a real video with a known object movement such as a ball rolling and casting a shadow, then checking if the output matches the expected new position, orientation, and shadow placement without introducing artifacts.

Figures

Figures reproduced from arXiv: 2606.24225 by Chirui Chang, Haoru Tan, Jianmin Bao, Pengfei Wan, Shizhen Zhao, Xiaojuan Qi, Xiaoyang Lyu, Xin Tao, Yi-Hua Huang, Yikang Ding.

Figure 1
Figure 1. Figure 1: GIVE performs object-level geometric video editing using unified pre and post box-based geometry instructions derived from lightweight user interaction and off-the-shelf tools. A single Video DiT editor supports multiple DCC-style operators, including rotation, scaling, removal, translation, duplication, and trajectory editing, while maintaining temporal coherence and producing consistent geometry-dependen… view at source ↗
Figure 2
Figure 2. Figure 2: Limitation of reconstruction-based methods. Shape-for-Motion fails to recover a coherent proxy with complex motion, leading to unreliable edits; GIVE remains stable without per-video reconstruction/optimization. MiniMax 2025; OpenAI 2025]. In practical DCC workflows, many revisions are object-level geometric operations: nudging a prop to refine composition, rotating an actor to face the camera, scaling an … view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of GIVE. The input RGB video and four geometry￾instruction streams are encoded by a shared video VAE into spatiotemporal tokens. These tokens are concatenated with the noisy edited-video latent and denoised by a video diffusion transformer with RoPE state alignment. The predicted edited latent is decoded to produce the edited video. streams, which are later tokenized as geometry-instruction (G… view at source ↗
Figure 4
Figure 4. Figure 4: Procedural Construction Pipeline. From left to right, (i) Asset Sampling: selection of a scene and a target actor; (ii) Attributes Sampling: assignment of actor attributes independent of the edit (e.g., facial expressions and body-motion clips); (iii) Camera Sampling: sampling of a camera-motion family and continuous parameters to define the camera trajectory; (iv) Operator Sampling: sampling of a DCC-styl… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on real videos (I). We show results for geometric operators including removal, rotation and translation on the real videos. Across cases, GIVE better follows the intended geometric manipulation while preserving object identity and non-edited regions, and produces more coherent geometry-dependent secondary effects (e.g., shadows and reflections). Baselines often exhibit operator mis-e… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on real videos (II). We show results for geometric operators including duplication, scaling and trajectory editing on the real videos. GIVE consistently achieves more faithful geometric edits with stronger temporal coherence, while baselines may introduce identity/background drift, operator mis-execution, or temporally inconsistent artifacts. For trajectory editing, arrows in the inp… view at source ↗
read the original abstract

Object-level geometric edits, including translating, rotating, scaling, duplicating, or removing an object, are routine operations in digital content creation (DCC) workflows, yet they remain unreliable in generative video editing. The key challenge lies in specifying the target object's 3D state change unambiguously across viewpoint and time, while consistently updating geometry-dependent secondary effects such as shadows and reflections. We introduce GIVE, a geometry-instructed video editing framework that represents edits through a unified object-state formulation. Two video-aligned geometry streams describe the target object before and after editing: a depth-box encoding coarse 3D placement and extent, and an orientation-box providing an appearance-agnostic orientation cue. Together, these streams provide a compact pre/post geometric specification for object-state transitions. To provide paired supervision for learning these edits, we build a scalable graphics-engine pipeline that executes object-level edit programs and renders controlled before/after pairs, isolating the intended geometric edit while keeping secondary effects consistent with the transformation. Experimental results demonstrate that GIVE produces faithful geometric edits with temporal coherence and consistent secondary effects across operators in a unified framework, and shows promising transfer to in-the-wild videos. Project page: https://geometry-instructed-video-editing.github.io/give/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces GIVE, a geometry-instructed video editing framework that specifies object-level edits (translate, rotate, scale, duplicate, remove) via a unified object-state formulation. Two video-aligned geometry streams—a depth-box for coarse 3D placement/extent and an orientation-box for appearance-agnostic orientation—provide compact pre/post specifications. A graphics-engine pipeline generates paired before/after videos by executing edit programs while aiming to keep secondary effects (shadows, reflections) consistent. The central claim is that this yields faithful geometric edits with temporal coherence and consistent secondary effects in a single framework, plus promising transfer to in-the-wild videos.

Significance. If the synthetic-to-real transfer and coherence claims hold with supporting evidence, the work would address a practical gap in generative video editing by enabling explicit, controllable 3D geometric manipulations without per-operator retraining. The scalable synthetic supervision pipeline is a potential strength for avoiding the need for real paired data.

major comments (2)
  1. [Abstract] Abstract (pipeline paragraph): the assertion that the graphics-engine pipeline 'isolates the intended geometric edit while keeping secondary effects consistent' is load-bearing for the transfer claim, yet no details are given on randomization of lighting, materials, or camera parameters, nor any measure of resulting domain gap; this leaves the weakest assumption (synthetic pairs generalize without lighting correlations or artifacts) untested.
  2. [Experimental results] Experimental results section: the abstract states that results 'demonstrate' faithful edits, temporal coherence, and consistent secondary effects, but supplies no quantitative metrics, ablation tables, or comparisons; without these it is impossible to verify whether the unified framework actually outperforms operator-specific baselines or preserves secondary effects on real inputs.
minor comments (2)
  1. [Method] Clarify the precise encoding and alignment procedure for the depth-box and orientation-box streams when they are introduced in the method section.
  2. [Related work] Add a reference to prior synthetic-to-real video editing works that address domain gap (e.g., via domain randomization or style transfer) to contextualize the pipeline design.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point-by-point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract (pipeline paragraph): the assertion that the graphics-engine pipeline 'isolates the intended geometric edit while keeping secondary effects consistent' is load-bearing for the transfer claim, yet no details are given on randomization of lighting, materials, or camera parameters, nor any measure of resulting domain gap; this leaves the weakest assumption (synthetic pairs generalize without lighting correlations or artifacts) untested.

    Authors: The abstract summarizes the pipeline at a high level due to length constraints. The full manuscript (Section 3) details the graphics-engine pipeline, including randomization over lighting, materials, and camera parameters to isolate geometric edits and reduce domain gap. We will revise the abstract to briefly note the domain randomization strategy and add a short domain-gap analysis or reference in the revised version. revision: yes

  2. Referee: [Experimental results] Experimental results section: the abstract states that results 'demonstrate' faithful edits, temporal coherence, and consistent secondary effects, but supplies no quantitative metrics, ablation tables, or comparisons; without these it is impossible to verify whether the unified framework actually outperforms operator-specific baselines or preserves secondary effects on real inputs.

    Authors: The experimental section presents qualitative results and visual comparisons across edit operators and in-the-wild videos. We agree that quantitative metrics and ablations would strengthen verification. We will add ablation tables, temporal coherence metrics, and a user study on secondary effects in the revision; direct baseline comparisons will also be included where feasible. revision: partial

Circularity Check

0 steps flagged

No circularity: explicit geometry streams and separate rendering pipeline are independent of the learned editing model.

full rationale

The derivation introduces depth-box and orientation-box as a compact pre/post geometric specification, then builds an independent graphics-engine pipeline to generate paired supervision. The central claim (faithful geometric edits with temporal coherence) is evaluated on outputs of this pipeline and on in-the-wild transfer; nothing in the abstract or described chain reduces the model output to a quantity defined by the same inputs or by self-citation. The pipeline is presented as an external data-generation step rather than a fitted component renamed as prediction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only; no free parameters, axioms, or invented entities are described.

pith-pipeline@v0.9.1-grok · 5774 in / 1130 out tokens · 20325 ms · 2026-06-26T00:41:09.493702+00:00 · methodology

discussion (0)

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