Research map / Probabilistic and scientific modeling / Variational and generative sampling
ShapeOdds: Variational Bayesian Learning of Generative Shape Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn a Bayesian generative shape model directly in silhouette space | ShapeOdds · Weizmann horse and Caltech-101 motorbike | generalization cross-entropy | Helped |
| Use ShapeBM instead of ShapeOdds | Weizmann horse · Weizmann horse, N = 49 | generalization cross-entropy | Helped |
| Add robust inference using a robust loss | ShapeOdds · Corrupted Weizmann horse silhouettes | recovery cross-entropy | Helped |
Closest papers by meaning
- Infinite ShapeOdds: Nonparametric Bayesian Models for Shape Representations (AAAI 2020 · similarity 0.92)
- ShapeShifter: 3D Variations Using Multiscale and Sparse Point-Voxel Diffusion (CVPR 2025 · similarity 0.85)
- GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors (CVPR 2024 · similarity 0.85)
- Mosaic-SDF for 3D Generative Models (CVPR 2024 · similarity 0.85)
- Flow-Corrected Shape Optimization: Taming Manifold Drift in High-Dimensional 3D Models (NeurIPS 2026 · similarity 0.85)
- $\texttt{NAISR}$: A 3D Neural Additive Model for Interpretable Shape Representation (ICLR 2024 · similarity 0.85)
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