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Over++: Generative Video Compositing for Layer Interaction Effects

Luchao Qi, Jiaye Wu, Jun Myeong Choi, Cary Phillips, Roni Sengupta, Dan B Goldman

arXiv:2512.19661v1cs.CV

TL;DR

Professional compositing requires environmental interactions between foreground and background, but current tools leave substantial manual effort and control challenges. Over++ introduces text- and mask-conditioned augmented compositing, trained with paired and unpaired data. The paper reports realistic effects with input-content preservation and improvements over previous approaches, while identifying fidelity and challenging-background limits.

  • Problem

    Compositing environmental interactions while preserving the input video remains difficult: generative models resist controlled workflows, and inpainting methods struggle with complex effects and require per-frame masks.

    Source: §1 · pp. 1–2

  • Method

    Over++ fine-tunes a video inpainting diffusion model to add text-consistent effects within optional masks while preserving the composite’s appearance and motion.

    Source: §3.1 · p. 3

  • Results

    Over++ produces realistic effects while preserving original video content, and the paper reports that it outperforms previous approaches for effect generation and scene preservation.

    Source: §4.1 · p. 6, §6 · p. 8

  • Takeaways & Limitations

    The method supports diverse real-world effect generation, including shadows, dust, smoke, and water splashes, despite limited training data.

    Source: §1 · p. 2

    Over++ may not produce pixel-perfect reconstructions and may hallucinate implausible effects in challenging background regions.

    Source: §6 · p. 8

Abstract

from arXiv · show

In professional video compositing workflows, artists must manually create environmental interactions-such as shadows, reflections, dust, and splashes-between foreground subjects and background layers. Existing video generative models struggle to preserve the input video while adding such effects, and current video inpainting methods either require costly per-frame masks or yield implausible results. We introduce augmented compositing, a new task that synthesizes realistic, semi-transparent environmental effects conditioned on text prompts and input video layers, while preserving the original scene. To address this task, we present Over++, a video effect generation framework that makes no assumptions about camera pose, scene stationarity, or depth supervision. We construct a paired effect dataset tailored for this task and introduce an unpaired augmentation strategy that preserves text-driven editability. Our method also supports optional mask control and keyframe guidance without requiring dense annotations. Despite training on limited data, Over++ produces diverse and realistic environmental effects and outperforms existing baselines in both effect generation and scene preservation.

1. Introduction

Layer compositing combines foreground and background, but adding environmental interactions still demands substantial manual work. Over++ frames augmented compositing as text-guided effect generation that preserves the input scene.

1. Introduction

  • Over++ is introduced as a generative extension to layer compositing, whose “over” operator combines image elements with pre-multiplied alpha.The operator was formalized by Porter and Duff.Source: §1 · p. 1
  • Generative video models can produce realistic content but their stochastic behavior resists the controlled, iterative workflows compositors need, while inpainting struggles with complex effects and requires per-frame masks.Traditional compositing tools also require substantial manual effort.Source: §1 · pp. 1–2
  • Augmented compositing uses foreground and background elements, a text prompt, and an optional mask video to add semi-transparent environmental interactions without fundamentally altering other elements.Preserving scene appearance supports continuity across a sequence and adherence to filmmakers’ directorial intent.Source: §1 · p. 2
  • Over++ combines paired synthetic and real-world videos with unpaired effect videos, then extends video generative models to generate effects with optional mask guidance while preserving instruction-following capabilities.The training set comprises 54 real-world paired, 573 synthetic paired, and 460 unpaired videos; the paper reports generalization to shadows, dust, smoke, and water splashes.Source: §1 · p. 2

2. Related Work

Prior VFX and generative approaches address parts of effect creation or video editing, but the paper distinguishes augmented compositing by its focus on controllable environmental interactions between preserved foreground and background layers.

Related work

  • Traditional VFX simulation and manual compositing can be realistic but demand time, computation, and expertise; recent generative VFX work targets stylized effects rather than physically grounded environmental interactions.The cited generative efforts include approaches with and without effect references [45] [48].Source: §2 · p. 2
  • Inpainting models such as VACE [32] require per-frame masks and may still fail to generate the desired effect, as illustrated by the target wake comparison.Simply compositing foreground over background also produces an input without effects.Source: Figure 2 · p. 2
  • Omnimatte-style decomposition separates layers rather than composing them and lacks explicit controllability, whereas Over++ targets joint spatial-mask and text control over environmental effects.The paper contrasts its task with image and video decomposition approaches [10] [39] [72].Source: §2 · p. 2
  • Video editing models primarily target object- or appearance-level changes, while ActAnywhere [53] does not directly support professional compositing with a dynamic background video.The paper’s setting instead synthesizes environmental effects between separate layers without altering their original content or motion.Source: §2 · pp. 2–3

3. Method

Over++ treats environmental-effect synthesis as video inpainting conditioned on a composite, optional spatial masks, and text. Its training combines paired effect data, mask strategies, and unpaired videos to support mask- and prompt-guided generation.

3. Method

  • Over++ is trained mainly on synthetic data with limited real-world examples and is described as generalizing to in-the-wild videos with realistic, prompt-guided effects in human-annotated masks.The method overview frames augmented compositing as a variation of video inpainting.Source: §3 · p. 3

3.1. Task Formulation

  • The task is to add text-consistent, visually plausible effects within a specified mask while preserving the composite’s original appearance and motion.Over++ is a video inpainting diffusion model conditioned jointly on the effect mask and text prompt.Source: §3.1 · p. 3

3.2. Dataset

  • To address scarce paired videos, the dataset construction decomposes effect videos into foreground-with-effects and clean background layers, then recomposites the extracted subject over the clean background.The resulting corpus includes 54 real-world paired videos, 573 synthetic paired videos, and 460 unpaired videos; Figure 4 illustrates the mask-generation context.Source: §3.2 · pp. 3–4, Figure 4 · p. 4

3.3.1. Mask-based Effect Generation

  • Mask pruning thresholds the grayscale difference between paired videos and applies erosion, dilation, and median filtering to reduce noise, especially in real-world data.The resulting masks may remain imperfect, which the authors describe as natural augmentation that supports coarse hand-drawn masks.Source: §3.3.1 · pp. 4–5

3.3.2. Prompt-based Effect Generation

  • A tri-mask design trains with masked and unmasked conditions, enabling mask-guided, mask-free, and mixed keyframe guidance within one model.Unpaired videos and augmented captions preserve text-to-video capabilities and prompt-editing capacity, with missing input-video and mask latents zeroed during training.Source: Figure 3 · p. 4, §3.3.1 · p. 5, §3.3.2 · p. 5

3.4. Implementation Details

  • The experiments fine-tune CogVideoX-5B and train with an L2 diffusion loss at 384×672 resolution for 1,000 iterations on eight NVIDIA A6000 GPUs.Temporal multidiffusion is used at inference for videos longer than 85 frames.Source: §3.4 · p. 5

4. Results

Over++ achieves state-of-the-art overall performance while preserving source content, and user-study and ablation results support its effect fidelity and prompt-editing capability.

4.1. Qualitative Comparison

  • Over++ generates wake, shadow, and reflection effects while preserving subjects and composition, unlike alternatives that alter source appearance.The comparison includes state-of-the-art methods [16, 34] and Runway Aleph.Source: Figure 5 · p. 6, §4.1 · p. 6

4.2. Quantitative Comparison

  • Over++ achieves state-of-the-art overall performance on effect generation, though subtle or localized effects yield only marginal gains on conventional CLIP-based metrics.Table 1 reports the quantitative comparison; the supplied passages do not provide its cell values.Source: §4.2 · p. 6, Table 1 · p. 6
  • Over++ supports mask guidance, text-prompt editing, and CFG scaling across DAVIS, in-the-wild, synthetic, and mixed-source videos.Figure 6 illustrates smoke, splash and reflection, wake, and splash generation.Source: Figure 6 · p. 7

4.3. User Study

  • In a 30-participant user study, Over++ achieves quality comparable to Runway Aleph while improving input-video adherence and explicit effect control.Participants included 14 professional VFX artists and 16 non-expert users.Source: §4.3 · p. 7, Table 2 · p. 7

4.4. Ablation Study

  • Adding unpaired data improves prompt editing; without it, the model cannot follow the prompt and achieves the lowest CLIPtext score.The paired-data-only ablation loses the base model’s prompt adherence.Source: §4.4 · p. 8, Figure 7 · p. 8

5. Extensions and Applications

Over++ supports text-driven effect variations, robust editing with imperfect masks, and keyframe mask guidance. These controls allow edits to effect style, location, and strength without requiring annotations on every frame.

Text Prompt Editing

  • Text prompts enable fine-grained modulation of effect intensity and style, including color changes and stylistic variations.The examples include changing generated smoke color and producing soft or harsh shadows.Source: §5 · p. 8, Figure 8 · p. 8

Robust Mask Editing

  • Over++ handles masks that include the foreground or indicate physically implausible effect regions while maintaining semantic consistency.Examples show it adding a shadow without altering the foreground and ignoring a spurious circular region while adding a reflection.Source: §5 · p. 8, Figure 9 · p. 8

Keyframe Mask Annotations

  • A single keyframe mask can guide effect strength while Over++ interpolates smoothly across unannotated frames.The example shows stronger water splashes under keyframe guidance and mild splashes in unannotated frames.Source: §5 · p. 8, Figure 10 · p. 8

6. Discussion and Limitations

Over++ preserves input content but may not achieve pixel-perfect reconstruction, and it can hallucinate implausible effects in challenging backgrounds. The method does not address harmonization or relighting.

Limitations

  • VAE encoding and decoding may prevent pixel-perfect reconstruction despite Over++'s state-of-the-art input-content preservation.The authors suggest per-example test-time optimization or fidelity-enhancement modules as possible future directions.Source: §6 · p. 8
  • Over++ may hallucinate implausible effects in challenging background regions.The authors suggest stronger pretrained priors as a possible way to improve robustness.Source: §6 · p. 8

Discussion

  • The method introduces augmented compositing and outperforms previous approaches, but does not address harmonization or relighting.Source: §6 · p. 8

Supplementary Material

The supplemental PDF points readers to additional visual materials on the project webpage.

Supplementary Materials

  • Additional images and videos are available on the project webpage for a more comprehensive evaluation of the visual results.Source: Supplementary Material · p. 13

A. Overview of Appendices

The appendices provide supplementary material on evaluation, training data, caption prompts, and failure cases.

Appendix B

Appendix C

Appendix D

Appendix E

  • Appendix E gives the caption-augmentation prompt used to create unpaired text-to-video data, complementing the method in Section 3.3.Source: A. Overview of Appendices · p. 13

Appendix F

B. Metrics Analysis

CLIP similarity may fail to reflect inserted environmental effects, sometimes scoring an image without the effect as more similar than one with the correct effect.

B. Metrics Analysis

  • CLIP similarity can favor images without an effect over perceptually faithful images containing the correct effect.The analysis compares generated images with and without added effects against a reference image and prompt caption.Source: Figure 11 · p. 13, B. Metrics Analysis · p. 13

C. Ablations of Training Data

The section examines how real and synthetic data contribute to effect generation, then describes caption generation, refinement, and augmentation for unpaired text-to-video data.

C. Ablations of Training Data

  • Removing synthetic data weakens shadow generation, while removing real data reduces complex effects such as reflections and water splashes.The ablation evaluates data-source contributions across three CLIP-based metrics; the qualitative figure highlights outputs from the full dataset.Source: C. Ablations of Training Data · p. 13, Figure 12 · p. 14, Table 3 · p. 14

D. Video Caption Generation

  • MiniCPM-V-2.6 produces dense spatio-temporal descriptions, which LLaMA-3.1-8B-Instruct refines into concise video-level captions retaining environmental cues.Source: D. Video Caption Generation · p. 14

E. Video Caption Augmentation

  • The pipeline generates multiple augmented captions from an initial caption to create diverse unpaired text-to-video data.The example varies the smoke’s style while keeping the car drift scene.Source: E. Video Caption Augmentation · p. 14

D.1 System Prompt for Caption Generation

  • Caption-generation prompts ask for effect type and intensity, interactions with scene elements, spatial extent, and temporal behavior.Source: §D.1 · p. 14

D.2 System Prompt for Caption Refinement (T )

  • Caption refinement prompts emphasize physically plausible foreground-background interactions and precise descriptions of effect intensity and spread.Source: §D.2 · p. 14

F. Failure Cases

Over++ can introduce unintended effects in background regions, and excessive classifier-free guidance can alter output color tone. Caption augmentation varies effect attributes while preserving the scene and actions.

F. Failure Cases

  • Unintended background effects can occur, and excessively high CFG can alter the output’s overall color tone.The figure also shows unwanted dust in challenging background regions.Source: F. Failure Cases · p. 15, Figure 13 · p. 15

F.1 System Prompt for Caption Augmentation (T )

  • Caption augmentation keeps the scene, subjects, and actions fixed while varying effect style, including color, intensity, spread, and texture.Source: §F.1 · p. 15

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