15 citations · 32 across the 12 of their papers we have counts for
14 papers
Quantum-Aware Key Rank for Side-Channel Attacks evaluation
Yevgen Kotukh
Side-channel attacks on block ciphers rarely recover the key outright: they leave a residual key space ordered by subkey posteriors, and evaluation laboratories report the key rank…
GPIC: A Giant Permissive Image Corpus for Visual Generation
Keshigeyan Chandrasegaran, Kyle Sargent, Suchir Agarwal +6
Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 tri…
Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation
Alan Baade, Eric Ryan Chan, Kyle Sargent +4
Latent diffusion models excel at generating high-quality images but lose the benefits of end-to-end modeling. They discard information during image encoding, require a separately t…
VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression
Kyle Sargent, Ruiqi Gao, Philipp Henzler +5
Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human pe…
Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Kyle Sargent, Kyle Hsu, Justin Johnson +2
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems tha…
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Stephen Tian, Blake Wulfe, Kyle Sargent +4
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, envi…
Generative Camera Dolly: Extreme Monocular Dynamic Novel View Synthesis
Basile Van Hoorick, Rundi Wu, Ege Ozguroglu +6
Accurate reconstruction of complex dynamic scenes from just a single viewpoint continues to be a challenging task in computer vision. Current dynamic novel view synthesis methods t…
WonderJourney: Going from Anywhere to Everywhere
Hong-Xing Yu, Haoyi Duan, Junhwa Hur +8
We introduce WonderJourney, a modularized framework for perpetual 3D scene generation. Unlike prior work on view generation that focuses on a single type of scenes, we start at any…
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Kyle Sargent, Zizhang Li, Tanmay Shah +8
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 b…
NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt +13
Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on…
VQ3D: Learning a 3D-Aware Generative Model on ImageNet
Kyle Sargent, Jing Yu Koh, Han Zhang +5
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…
Self-supervised AutoFlow
Hsin-Ping Huang, Charles Herrmann, Junhwa Hur +5
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. Obse…
Pyramid Adversarial Training Improves ViT Performance
Charles Herrmann, Kyle Sargent, Lu Jiang +5
Aggressive data augmentation is a key component of the strong generalization capabilities of Vision Transformer (ViT). One such data augmentation technique is adversarial training…
SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
Varun Jampani, Huiwen Chang, Kyle Sargent +8
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches combine monocular depth networks with inpainting networks to achieve comp…