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TransAnimate: Taming Layer Diffusion to Generate RGBA Video

Xuewei Chen, Zhimin Chen, Yiren Song

arXiv:2503.17934v1cs.CV

TL;DR

RGBA video generation is challenged by scarce training data and the difficulty of adapting transparent-image models to video. TransAnimate combines transparent-image and RGB-pretrained video components, complementary RGBA data, and motion-and-color controls; the paper reports diverse RGBA generation with controllability comparable to RGB video approaches.

  • Problem

    RGBA video generation remains underdeveloped because large-scale RGBA datasets are scarce and transparent-image models do not integrate seamlessly with video-generation models.

    Source: §1 · p. 2

  • Method

    TransAnimate adapts transparent-image generation with RGB-pretrained temporal and control components, using curated, segmented, and synthetic RGBA videos plus motion-and-color controls.

    Source: §1 · p. 2

  • Results

    TransAnimate generates diverse RGBA videos while maintaining controllability comparable to RGB video approaches, with qualitative results reporting fine details, structural consistency, and fidelity to user controls.

    Source: §1 · p. 2, §4.2 · p. 8, §4.3 · p. 8

  • Takeaways & Limitations

    The framework provides motion-direction and scaling controls tailored to game-effect creation and is presented as a tool for customizable content in gaming and visual effects.

    Source: §5 · p. 9

Abstract

from arXiv · show

Text-to-video generative models have made remarkable advancements in recent years. However, generating RGBA videos with alpha channels for transparency and visual effects remains a significant challenge due to the scarcity of suitable datasets and the complexity of adapting existing models for this purpose. To address these limitations, we present TransAnimate, an innovative framework that integrates RGBA image generation techniques with video generation modules, enabling the creation of dynamic and transparent videos. TransAnimate efficiently leverages pre-trained text-to-transparent image model weights and combines them with temporal models and controllability plugins trained on RGB videos, adapting them for controllable RGBA video generation tasks. Additionally, we introduce an interactive motion-guided control mechanism, where directional arrows define movement and colors adjust scaling, offering precise and intuitive control for designing game effects. To further alleviate data scarcity, we have developed a pipeline for creating an RGBA video dataset, incorporating high-quality game effect videos, extracted foreground objects, and synthetic transparent videos. Comprehensive experiments demonstrate that TransAnimate generates high-quality RGBA videos, establishing it as a practical and effective tool for applications in gaming and visual effects.

1. Introduction

TransAnimate addresses the scarcity of RGBA video data and the difficulty of adapting transparent-image and video-generation models into a controllable framework. It combines complementary data sources, RGB-pretrained video components, and motion-and-color controls for game effects.

1. Introduction

  • RGBA video synthesis is constrained by scarce datasets and the difficulty of combining transparent-image generation with video models.The paper frames using RGB video pretraining and RGBA image models efficiently as the core challenge.Source: §1 · p. 2
  • TransAnimate combines AnimateDiff motion modeling, LayerDiffuse transparency generation, and sparse-control methods through adaptation layers fine-tuned on limited RGBA video data.The framework is presented as unifying transparency modeling and video generation.Source: §1 · p. 2
  • Three data strategies contribute different strengths: curated game videos offer quality, segmented foregrounds add motion diversity, and synthetic videos provide precise edges and broad category coverage.The sources also have limitations: curated footage lacks category diversity, segmented foregrounds can have incomplete objects and imperfect edges, and synthetic motion is less complex.Source: §1 · p. 2
  • A positive-trigger strategy assigns learnable tokens to data sources to mitigate unwanted features from imperfect data distributions.High-quality results from models trained on these datasets are manually curated and added as supplementary data through iterative refinement.Source: §1 · p. 2
  • LayerDiffuse is augmented with temporal modules initialized from AnimateDiff’s RGB-pretrained weights, then fine-tuned on RGBA videos; RGB-pretrained SparseCtrl is adapted by input-layer fine-tuning.The paper reports that minimal parameter adjustments suffice for high-consistency control.Source: §1 · p. 2
  • Motion vectors provide 8-directional trajectory control with velocity parameterization, while chromatic scaling regulates effect magnitude.The paper describes this as a joint vector-chroma control system for game visual effects.Source: §1 · p. 2
  • The framework targets temporally coherent layered transparent videos and pixel-accurate motion specification for game effects.Its dataset pipeline integrates game effects, segmented foregrounds, and synthetic videos, refined iteratively with positive triggers.Source: §1 · p. 2

2. Related Work

Prior work advances text-to-video generation, explicit control, and transparent-layer processing, but transparent video generation remains underexplored. TransAnimate applies these directions to game-effect videos and illustrates its framework and three-source dataset.

2. Related Work

  • Text-to-video methods build on diffusion and pretrained image generators; AnimateDiff adds modular motion layers, while newer approaches use spatiotemporal architectures or video tokens.The related-work discussion cites Video Diffusion Model [17], Stable Diffusion [33], AnimateDiff [12], W.A.L.T. [14], Sora [4], and VideoPoet [23].Source: §2 · pp. 2–3
  • Figure 2 shows frozen transparent encoding and decoding around temporal-attention and linear layers, with pretrained SparseCtrl weights providing motion, sketch, and RGB-image control.Source: Figure 2 · p. 3
  • Control methods use depth, skeleton, and image guidance [7] [8] [11] [20] [37] [45], while camera-control approaches include AnimateDiff [12], Direct-a-Video [41], and MotionCtrl [39].TransAnimate instead targets predefined motion directions and scales for transparent-video generation.Source: §2 · p. 3
  • Transparent-layer research centers on image matting and diffusion-based image generation, including latent transparency in LayerDiffuse [43]; SAM-2 [31] provides video segmentation, not video generation.The paper says SAM-2 struggles to create diverse, high-quality transparent layers for effects such as fire, light, and smoke.Source: §2 · p. 3
  • The paper positions its framework as a game-effect tool supporting text-to-RGBA generation and multiple control modalities.It describes the aim as creating dynamic transparent layers for gaming applications.Source: §2 · pp. 3–4
  • Figure 3 presents three dataset sources: game effects, segmented foregrounds, and synthesized motion videos, with synthesized examples ordered as motion caption, raw image, synthetic output, and motion control.The illustrated sets contain 3,000, 7,000, and 20,000 videos, respectively.Source: Figure 3 · p. 4

3. Method

TransAnimate combines transparent-image generation with video motion modeling and control, using an RGBA dataset to adapt RGB-pretrained components for transparent video generation.

3. Method

  • TransAnimate’s method combines LayerDiffuse transparency generation, AnimateDiff motion modeling, RGBA video data, and adapted sparse control.The framework is designed to learn transferable motion priors while using text-to-transparent-image generation for transparent video.Source: §3 · p. 4

3.1. Preliminary

  • Stable Diffusion diffuses in autoencoder latent space, while AnimateDiff adds a temporal motion module to an image model to capture inter-frame dependencies.AnimateDiff’s temporal Transformer uses self-attention and positional encodings to process frame sequences.Source: §3.1 · p. 4

3.2. New Dataset for Transparent Video Generation

  • The dataset combines 3,000 curated game-effect videos, 7,000 extracted foreground videos, and 20,000 synthetic transparent videos, with iteration adding curated generated results.Synthetic videos use translation, scaling, and rotation; the three data sources contribute transparency quality, motion and appearance diversity, and motion control.Source: §3.2 · pp. 4–5

3.3. Positive Triggers

  • Positive triggers target complementary qualities: edge quality for Synthetic Dataset samples and motion quality for Foreground Dataset samples.At inference, combining both triggers is reported to improve sharpness and dynamic consistency.Source: §3.3 · pp. 5–6

3.4. Text2RGBA Video Generation

  • LayerDiffuse is extended with a separately trained motion module while its text-to-transparent-image network stays frozen, preserving transparency during temporal animation.The motion module captures temporal dependencies through interactions across frame features.Source: §3.4 · pp. 6–7

3.5. Controllable RGBA Video Generation

  • For controllable generation, pretrained SparseCtrl weights remain frozen while its Adapter is fine-tuned on RGBA videos using RGB-channel controls.The controls include motion, RGB image, and sketch inputs; arrows specify movement direction and colors indicate effect scale.Source: §3.5 · p. 7
  • Sketch controls can specify a layout with one sketch or guide coarse motion and transitions with sketches for initial, final, and intermediate frames.The paper also describes RGB-conditioned animation, transitions, prediction, and interpolation as supported tasks.Source: §3.5 · pp. 7–8

4. Experiments

The experiments assess RGBA video quality, controllable generation, and training choices using qualitative examples, quantitative comparisons, and a user study. Reported findings favor adapting RGB-pretrained control and using two-stage training and positive prompts.

4.1. Experiment Setting

  • The evaluation uses CLIP Score, FVD, and a 15-participant user study rating RGBA-edge quality and motion realism and smoothness.The study covers 50 generated videos; training uses 256 × 256 resolution for 16 frames and 3,000 iterations.Source: §4.1 · p. 8

4.2. Qualitative Results

  • Figure 4 illustrates transparency effects, fine detail, structural consistency, and generalization across varied content types.The authors describe the examples as demonstrating robustness and adaptability.Source: §4.2 · p. 8

4.3. Qualitative Controllable Results

  • Figure 5 shows transparent-image generation that maintains user-provided sketches, motion trajectories, or reference images as control inputs.The approach supports control over object structure, movement, and appearance.Source: §4.3 · p. 8

4.4. Ablative Study

  • Fine-tuned RGB-pretrained control adapts to RGBA video, unlike direct application of SparseControl weights, which fails to preserve structural consistency.The authors report superior alignment with the control image while maintaining transparency effects.Source: §4.4 · p. 8
  • Two-stage training improves final RGBA-video performance and is reported to increase dataset diversity and scalability without compromising visual fidelity.The authors also report more stable, high-quality video generation.Source: §4.4 · p. 8
  • Positive prompts are reported to improve overall RGBA-video quality, motion coherence, and edge fidelity while preserving structural integrity.The authors describe more consistent and visually refined outputs.Source: §4.4 · p. 8

5. Conclusion

TransAnimate combines transparency modeling and motion dynamics to generate temporally coherent layered videos, and uses a three-step strategy to build RGBA video data. Its motion-guided control adjusts direction and scale for game-effect creation, with RGB control inputs supporting generation when RGBA data are limited.

5. Conclusion

  • TransAnimate combines transparency modeling with motion dynamics to generate high-quality, layered transparent videos with temporal coherence.The conclusion frames this as addressing the underexplored challenge of RGBA video generation.Source: §5 · p. 8
  • Its three-step data strategy uses high-quality game-effect videos, extracted foreground objects, and synthetic transparent videos with controlled motion dynamics.Source: §5 · p. 8
  • Motion-guided control adjusts direction and scaling for game effects; RGB-channel control inputs leverage pretrained RGB models despite limited RGBA data.The authors describe the framework as extending controllability to layered transparency for gaming and visual effects.Source: §5 · p. 9

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