Wan-Alpha++: Animating a Single RGBA Asset into Controllable Transparent Video Effects

Key points

• RGBA video generation is essential for creating compositable visual assets in filmmaking, game development, and digital content production.1
• Existing text-to-RGBA video methods often entangle RGB and alpha distributions and offer limited control over the precise appearance of a target asset.1
• Specifically, we introduce a transparency-aware bidirectional diffusion objective to produce distinguishable RGBA latent representations, and a transparency-guided mean sampler that shifts the noise distribution to improve transparency generation and enable spatial controllability.1
• Building upon this RGBA generation prior, we introduce temporal phase reparameterization for image-conditioned effect generation.1

Sources (1)

• [1]Wan-Alpha++: Animating a Single RGBA Asset into Controllable Transparent Video Effects
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 11:18 AM

  To address these issues, we present Wan-Alpha++, a unified framework that brings controllable RGBA video generation to both text- and image-to-video diffusion backbones through a shared RGBA latent space, extending our conference version Wan-Alpha from the text-conditioned setting to image-conditioned generation with an interior reference frame.

  RGBA video generation is essential for creating compositable visual assets in filmmaking, game development, and digital content production.

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