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Diffusion with forward models: Solving stochastic inverse problems without direct supervision
Denoising diffusion models are a powerful type of generative models used to capture
complex distributions of real-world signals. However, their applicability is limited to …
complex distributions of real-world signals. However, their applicability is limited to …
Craftsman: High-fidelity mesh generation with 3d native generation and interactive geometry refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate
high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed …
high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed …
DORSal: Diffusion for Object-centric Representations of Scenes et al
Recent progress in 3D scene understanding enables scalable learning of representations
across large datasets of diverse scenes. As a consequence, generalization to unseen …
across large datasets of diverse scenes. As a consequence, generalization to unseen …
Geometric Neural Process Fields
This paper addresses the challenge of Neural Field (NeF) generalization, where models
must efficiently adapt to new signals given only a few observations. To tackle this, we …
must efficiently adapt to new signals given only a few observations. To tackle this, we …
DORSal: Diffusion for Object-centric Representations of Scenes
Recent progress in 3D scene understanding enables scalable learning of representations
across large datasets of diverse scenes. As a consequence, generalization to unseen …
across large datasets of diverse scenes. As a consequence, generalization to unseen …