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Found 31 papers, 20 papers with code
Text2Mesh: Text-Driven Neural Stylization for Meshes
In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.
Ranked #1 on
Neural Stylization
on Meshes
Image-Based CLIP-Guided Essence Transfer
We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target.
One-Sided Unsupervised Domain Mapping
In this work, we present a method of learning $G_{AB}$ without learning $G_{BA}$.
Ranked #8 on
Facial Expression Translation
on CelebA
One-Shot Unsupervised Cross Domain Translation
Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B.
Emerging Disentanglement in Auto-Encoder Based Unsupervised Image Content Transfer
Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image.
Structural-analogy from a Single Image Pair
In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.
Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation
The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model.
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection
We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec.
Polynomial Neural Fields for Subband Decomposition and Manipulation
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
We consider the task of generating diverse and novel videos from a single video sample.
JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting
To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.
Mask Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
Masked Based Unsupervised Content Transfer
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
Domain Intersection and Domain Difference
We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain.
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
In the setting of robustness, our method improves on both ImageNet-C and Cifar-100-C for multiple architectures.
Discriminative Class Tokens for Text-to-Image Diffusion Models
This approach has two disadvantages: (i) supervised datasets are generally small compared to large-scale scraped text-image datasets on which text-to-image models are trained, affecting the quality and diversity of the generated images, or (ii) the input is a hard-coded label, as opposed to free-form text, limiting the control over the generated images.
Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music.
Estimating the Success of Unsupervised Image to Image Translation
While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way.
SpeedNet: Learning the Speediness in Videos
We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval.
Locally Shifted Attention With Early Global Integration
The separation of the attention layer into local and global counterparts allows for a low computational cost in the number of patches, while still supporting data-dependent localization already at the first layer, as opposed to the static positioning in other visual transformers.
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics.
Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs
The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications.
Unsupervised Learning of the Set of Local Maxima
Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1.
Evaluation Metrics for Conditional Image Generation
We present two new metrics for evaluating generative models in the class-conditional image generation setting.
Identity and Attribute Preserving Thumbnail Upscaling
We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes.
Local-Global Shifting Vision Transformers
Due to the expensive quadratic cost of the attention mechanism, either a large patch size is used, resulting in coarse-grained global interactions, or alternatively, attention is applied only on a local region of the image at the expense of long-range interactions.
On Disentangled and Locally Fair Representations
This problem is tackled through the lens of disentangled and locally fair representations.
Volumetric Disentanglement for 3D Scene Manipulation
To this end, we propose a volumetric framework for (i) disentangling or separating, the volumetric representation of a given foreground object from the background, and (ii) semantically manipulating the foreground object, as well as the background.
Text-Driven Stylization of Video Objects
The first target text prompt describes the global semantics and the second target text prompt describes the local semantics.
FewGAN: Generating from the Joint Distribution of a Few Images
We then use a separate set of side images to model the structure of generated images using an autoregressive model trained on the learned patch embeddings of training images.
Assessing Neural Network Robustness via Adversarial Pivotal Tuning
We demonstrate that APT is capable of a wide range of class-preserving semantic image manipulations that fool a variety of pretrained classifiers.
