SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
Is attention all that nerf needs?
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
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MU-GeNeRF combines source-view and target-view uncertainties via a heteroscedastic loss to enable distractor-aware generalizable NeRF reconstruction that matches scene-specific methods.
A feed-forward network predicts per-SMPL-X-vertex 3D Gaussians in canonical space from multi-view RGB images, enabling single-pass reconstruction and real-time animation via linear blend skinning.
RoSplat adds alpha normalization for brightness consistency across varying input views and a 3D sampling regularizer to mitigate hole artifacts in high-resolution feed-forward Gaussian splatting.
citing papers explorer
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SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis
SplatWeaver uses cardinality Gaussian experts and pixel-level routing to dynamically allocate varying numbers of Gaussian primitives for generalizable novel view synthesis.
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MU-GeNeRF: Multi-view Uncertainty-guided Generalizable Neural Radiance Fields for Distractor-aware Scene
MU-GeNeRF combines source-view and target-view uncertainties via a heteroscedastic loss to enable distractor-aware generalizable NeRF reconstruction that matches scene-specific methods.
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Real-Time Human Reconstruction and Animation using Feed-Forward Gaussian Splatting
A feed-forward network predicts per-SMPL-X-vertex 3D Gaussians in canonical space from multi-view RGB images, enabling single-pass reconstruction and real-time animation via linear blend skinning.
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RoSplat: Robust Feed-Forward Pixel-wise Gaussian Splatting for Varying Input Views and High-Resolution Rendering
RoSplat adds alpha normalization for brightness consistency across varying input views and a 3D sampling regularizer to mitigate hole artifacts in high-resolution feed-forward Gaussian splatting.