Interactive 3D Modeling with a Generative Adversarial Network

This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface. The user edits a voxel grid with a painting interface (like Minecraft). Yet, at any time, he/she can execute a SNAP command, which projects the current voxel grid onto a latent shape manifold with a learned projection operator and then generates a similar, but more realistic, shape using a learned generator network. Then the user can edit the resulting shape and snap again until he/she is satisfied with the result. The main advantage of this approach is that the projection and generation operators assist novice users to create 3D models characteristic of a background distribution of object shapes, but without having to specify all the details. The core new research idea is to use a GAN to support this application. 3D GANs have previously been used for shape generation, interpolation, and completion, but never for interactive modeling. The new challenge for this application is to learn a projection operator that takes an arbitrary 3D voxel model and produces a latent vector on the shape manifold from which a similar and realistic shape can be generated. We develop algorithms for this and other steps of the SNAP processing pipeline and integrate them into a simple modeling tool. Experiments with these algorithms and tool suggest that GANs provide a promising approach to computer-assisted interactive modeling.Comment: Published at International Conference on 3D Vision 2017 (http://irc.cs.sdu.edu.cn/3dv/index.html

Reconciling results of LSND, MiniBooNE and other experiments with soft decoherence

We propose an explanation of the LSND signal via quantum-decoherence of the mass states, which leads to damping of the interference terms in the oscillation probabilities. The decoherence parameters as well as their energy dependence are chosen in such a way that the damping affects only oscillations with the large (atmospheric) $\Delta m^2$ and rapidly decreases with the neutrino energy. This allows us to reconcile the positive LSND signal with MiniBooNE and other null-result experiments. The standard explanations of solar, atmospheric, KamLAND and MINOS data are not affected. No new particles, and in particular, no sterile neutrinos are needed. The LSND signal is controlled by the 1-3 mixing angle $\theta_{13}$ and, depending on the degree of damping, yields $0.0014 < \sin^2\theta_{13} < 0.034$ at $3\sigma$. The scenario can be tested at upcoming $\theta_{13}$ searches: while the comparison of near and far detector measurements at reactors should lead to a null-result a positive signal for $\theta_{13}$ is expected in long-baseline accelerator experiments. The proposed decoherence may partially explain the results of Gallium detector calibrations and it can strongly affect supernova neutrino signals.Comment: 20 pages, 3 figure