Student Success Board Game (Where at WOU?)

Student assignment: Create a board or video game which enhances or improves student engagement and success. Projects must include a visual component (working prototype) and four components of a game (voluntary participation, rules, goal, and feedback)

VQ3D: Learning a 3D-Aware Generative Model on ImageNet

Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human faces, animal faces, or cars. However, these models struggle on larger, more complex datasets. To model diverse and unconstrained image collections such as ImageNet, we present VQ3D, which introduces a NeRF-based decoder into a two-stage vector-quantized autoencoder. Our Stage 1 allows for the reconstruction of an input image and the ability to change the camera position around the image, and our Stage 2 allows for the generation of new 3D scenes. VQ3D is capable of generating and reconstructing 3D-aware images from the 1000-class ImageNet dataset of 1.2 million training images. We achieve an ImageNet generation FID score of 16.8, compared to 69.8 for the next best baseline method.Comment: 15 pages. For visual results, please visit the project webpage at http://kylesargent.github.io/vq3

Self-supervised AutoFlow

Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric. Observing a strong correlation between the ground truth search metric and self-supervised losses, we introduce self-supervised AutoFlow to handle real-world videos without ground truth labels. Using self-supervised loss as the search metric, our self-supervised AutoFlow performs on par with AutoFlow on Sintel and KITTI where ground truth is available, and performs better on the real-world DAVIS dataset. We further explore using self-supervised AutoFlow in the (semi-)supervised setting and obtain competitive results against the state of the art

ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Real Image

We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Our code and data are at http://kylesargent.github.io/zeronvs/Comment: 17 page

NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where Structure-from-Motion (SfM) techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose NAVI: a new dataset of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allow us to extract accurate derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: https://navidataset.github.ioComment: NeurIPS 2023 camera ready. Project page: https://navidataset.github.i

Salmonella and tomatoes

Outbreak information linking fresh tomato fruit to illnesses is reviewed in this chapter. While tomato fruit appear to support substantial proliferation of certain serovars of Salmonella enterica, detection of this pathogen in tomato plants prior to harvest is rare, and reports of Salmonella existence in tomato fruit still attached to field-grown plants are virtually non-existent. The bacterium is sensitive to UV and can be outcompeted by the native phytomicrobiota, which may explain its absence in field-grown crops. However, the persistence of certain serovars in fields and ponds of certain production areas is noted. Together with evidence of bacteria becoming internalized in tomato fruit during crop development likely through natural apertures, the presence of S. enterica in and around production fields suggests that an unusual weather event could lead to Salmonella contamination of fruit prior to harvest. The bacterium appears physiologically adaptive toward proliferation in tomato fruit. Once inside tomatoes, Salmonella is capable of sensing the availability of nutrients and physiological state of the fruit and differentially regulates specific genes. However, because Salmonella is an efficient nutrient scavenger, removal of multiple metabolic and regulatory genes was required to reduce its fitness within the fruit. Plants do not appear to recognize human enterics as pathogens, and their defenses treat them as endophytes

Rational Expectations Models with Higher Order Beliefs,” mimeo

Abstract This paper develops a general method of solving rational expectations models with higher order beliefs. Higher order beliefs are crucial in an environment with dispersed information and strategic complementarity, and the equilibrium policy depends on infinite higher order beliefs. It is generally believed that solving this type of equilibrium policy requires an infinite number of state variable

Rational Expectations Models with Higher Order Beliefs,” mimeo

Abstract This paper develops a general method of solving rational expectations models with higher order beliefs. Higher order beliefs are crucial in an environment with dispersed information and strategic complementarity, and the equilibrium policy depends on infinite higher order beliefs. It is generally believed that solving this type of equilibrium policy requires an infinite number of state variable