Trust3R introduces a gated residual refinement plus Normal-Inverse-Wishart evidential head that produces closed-form multivariate Student-t uncertainty for per-point geometry in feed-forward 3D reconstruction and improves uncertainty ranking metrics on indoor and outdoor benchmarks.
Vggt-world: Transforming vggt into an autoregressive geometry world model.arXiv preprint arXiv:2603.12655
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Envision4D presents a feed-forward 4D Gaussian Splatting framework with future pose prediction, temporal attention, and conditioned motion lifting for pose-free extrapolation in autonomous driving scenes.
Geometric 4D Stitching explicitly complements missing geometric regions in 4D generated scenes with grounded stitches to achieve consistent 4D representations in under 10 minutes on a single GPU.
SANA-WM is a 2.6B-parameter efficient world model that synthesizes minute-scale 720p videos with 6-DoF camera control, trained on 213K public clips in 15 days on 64 H100s and runnable on single GPUs at 36x higher throughput than prior open baselines.
citing papers explorer
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Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R
Trust3R introduces a gated residual refinement plus Normal-Inverse-Wishart evidential head that produces closed-form multivariate Student-t uncertainty for per-point geometry in feed-forward 3D reconstruction and improves uncertainty ranking metrics on indoor and outdoor benchmarks.
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Envision4D: Envisioning Visual Futures via Feed-forward 4D Gaussian Splatting for Autonomous Driving
Envision4D presents a feed-forward 4D Gaussian Splatting framework with future pose prediction, temporal attention, and conditioned motion lifting for pose-free extrapolation in autonomous driving scenes.
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Geometric 4D Stitching for Grounded 4D Generation
Geometric 4D Stitching explicitly complements missing geometric regions in 4D generated scenes with grounded stitches to achieve consistent 4D representations in under 10 minutes on a single GPU.
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SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer
SANA-WM is a 2.6B-parameter efficient world model that synthesizes minute-scale 720p videos with 6-DoF camera control, trained on 213K public clips in 15 days on 64 H100s and runnable on single GPUs at 36x higher throughput than prior open baselines.