InseRF: Text-Driven Generative Object Insertion in Neural 3D Scenes

no code implementations • 10 Jan 2024 • Mohamad Shahbazi, Liesbeth Claessens, Michael Niemeyer, Edo Collins, Alessio Tonioni, Luc van Gool, Federico Tombari

We introduce InseRF, a novel method for generative object insertion in the NeRF reconstructions of 3D scenes.

3D scene Editing Monocular Depth Estimation +2

NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions

1 code implementation • 22 Mar 2023 • Mohamad Shahbazi, Evangelos Ntavelis, Alessio Tonioni, Edo Collins, Danda Pani Paudel, Martin Danelljan, Luc van Gool

Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.

Image Generation Inductive Bias

Editing in Style: Uncovering the Local Semantics of GANs

2 code implementations • CVPR 2020 • Edo Collins, Raja Bala, Bob Price, Sabine Süsstrunk

Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image.

Disentanglement Image Generation

Detecting Memorization in ReLU Networks

no code implementations • ICLR 2019 • Edo Collins, Siavash Arjomand Bigdeli, Sabine Süsstrunk

We propose a new notion of `non-linearity' of a network layer with respect to an input batch that is based on its proximity to a linear system, which is reflected in the non-negative rank of the activation matrix.

Memorization

Deep Feature Factorization For Concept Discovery

2 code implementations • ECCV 2018 • Edo Collins, Radhakrishna Achanta, Sabine Süsstrunk

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images.

Unsupervised Facial Landmark Detection Unsupervised Human Pose Estimation +1

Preconditioned Spectral Descent for Deep Learning

no code implementations • NeurIPS 2015 • David E. Carlson, Edo Collins, Ya-Ping Hsieh, Lawrence Carin, Volkan Cevher

These challenges include, but are not limited to, the non-convexity of learning objectives and estimating the quantities needed for optimization algorithms, such as gradients.