VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders
Video generative models commonly rely on latent spaces learned by 3D Variational Autoencoders (3D-VAEs). However, conventional 3D-VAEs are mainly optimized for pixel-level reconstruction, which can limit the semantic and spatio-temporal structure captured by their latents. Meanwhile, Video Foundation Models (VFMs) such as V-JEPA 2 and VideoMAEv2 show strong video understanding capabilities, yet whether their frozen representations can be transformed into compact, reconstruction-capable, and generation-friendly video latents remains largely unexplored. We answer this question with VideoRAE, a r
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “VideoRAE: Taming Video Foundation Models for Generative Mode” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
“Shared author/contributor keys: jiang”
- FuzzySimilar title/name (fuzzy) · 59%Developer-Y/cs-video-courses →
“Fuzzy title match (0.73): “VideoRAE: Taming Video Foundation Models for Generative Mode” ≈ “Developer-Y/cs-video-courses””
- LinkedLinked via arxiv author · 85%Zhihao Xie →
“VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders”
- LinkedLinked via arxiv author · 85%Junfeng Wu →
“VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders”
- LinkedLinked via arxiv author · 85%Xinting Hu →
“VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders”
- LinkedLinked via arxiv author · 85%Junchao Huang →
“VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders”