SparseGen replaces dense volumetric or triplane representations with compact learned sparse 3D anchor queries expanded into Gaussians, trained via rectified-flow image reconstruction without 3D supervision to achieve faster, less biased image-to-3D generation.
Scalable Diffusion Models with Transformers
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World Machine is a latent-state transformer for generative time-series world modeling that claims better adaptation and lower scaling costs than standard transformers, validated on synthetic Toy1D data.
citing papers explorer
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Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias
SparseGen replaces dense volumetric or triplane representations with compact learned sparse 3D anchor queries expanded into Gaussians, trained via rectified-flow image reconstruction without 3D supervision to achieve faster, less biased image-to-3D generation.
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World Machine: Towards Generative World Modeling for Time-Series
World Machine is a latent-state transformer for generative time-series world modeling that claims better adaptation and lower scaling costs than standard transformers, validated on synthetic Toy1D data.