V-RAE: Rethinking Video Latent Spaces for Generation
Latent video generation relies on autoencoders to define a compact space in which generative models operate. Although video autoencoder architectures have evolved substantially, their latent spaces are still optimized primarily for pixel-level reconstruction and provide limited high-level semantic organization. A reconstruction-optimal latent space, however, need not be well suited to generative modeling. We propose V-RAE, a video representation autoencoder that builds compact generative latents on top of frozen vision foundation model representations. A lightweight temporal pooling module rem
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- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “V-RAE: Rethinking Video Latent Spaces for Generation” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Minghui Guo →
“V-RAE: Rethinking Video Latent Spaces for Generation”
- LinkedLinked via arxiv author · 85%Shengqiong Wu →
“V-RAE: Rethinking Video Latent Spaces for Generation”
- LinkedLinked via arxiv author · 85%Hao Fei →
“V-RAE: Rethinking Video Latent Spaces for Generation”