PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space
3D reconstruction and generation are commonly tackled by separate paradigms: pixel-based regression for reconstruction, and latent diffusion for generation. Recent works attempt to unify them in latent space, but with notable drawbacks: the diffusion objective is defined on latent features rather than the underlying 3D representation, and both branches suffer from information loss introduced by latent encoding, while requiring a pretrained Variational Autoencoder (VAE) or Representation Autoencoder (RAE). In this paper, we reformulate these two tasks under a unified pixel-space diffusion parad
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Sensen Gao →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- LinkedLinked via arxiv author · 85%Zhaoqing Wang →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- LinkedLinked via arxiv author · 85%Qihang Cao →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- LinkedLinked via arxiv author · 85%Dongdong Yu →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- LinkedLinked via arxiv author · 85%Changhu Wang →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- LinkedLinked via arxiv author · 85%Jia-Wang Bian →
“PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space”
- PossiblePossibly related (embedding) · 49%voxelmorph/voxelmorph →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”