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Found 13 papers, 2 papers with code
Probabilistic Directed Distance Fields for Ray-Based Shape Representations
One fundamental operation applied to such representations is differentiable rendering, as it enables inverse graphics approaches in learning frameworks.
PolyOculus: Simultaneous Multi-view Image-based Novel View Synthesis
This paper considers the problem of generative novel view synthesis (GNVS), generating novel, plausible views of a scene given a limited number of known views.
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
The resulting latent-space NeRF can produce novel views with higher quality than standard colour-space NeRFs, as the AE can correct certain visual artifacts, while rendering over three times faster.
Watch Your Steps: Local Image and Scene Editing by Text Instructions
A field is trained on relevance maps of training views, denoted as the relevance field, defining the 3D region within which modifications should be made.
Reference-guided Controllable Inpainting of Neural Radiance Fields
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools.
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields
We refer to this task as 3D inpainting.
Representing 3D Shapes with Probabilistic Directed Distance Fields
On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability.
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Under such a class of discriminator (or critic) functions, we present Gradient Normalization (GraN), a novel input-dependent normalization method, which guarantees a piecewise K-Lipschitz constraint in the input space.
Towards Robust Bisimulation Metric Learning
Learned representations in deep reinforcement learning (DRL) have to extract task-relevant information from complex observations, balancing between robustness to distraction and informativeness to the policy.
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape.
Cycle-Consistent Generative Rendering for 2D-3D Modality Translation
In the context of computer vision, this corresponds to a learnable module that serves two purposes: (i) generate a realistic rendering of a 3D object (shape-to-image translation) and (ii) infer a realistic 3D shape from an image (image-to-shape translation).
Geometric Disentanglement for Generative Latent Shape Models
Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics.
Latent Molecular Optimization for Targeted Therapeutic Design
We devise an approach for targeted molecular design, a problem of interest in computational drug discovery: given a target protein site, we wish to generate a chemical with both high binding affinity to the target and satisfactory pharmacological properties.
