Search Results for author:
Found 49 papers, 17 papers with code
Consolidating Attention Features for Multi-view Image Editing
In this work, we focus on spatial control-based geometric manipulations and introduce a method to consolidate the editing process across various views.
Contrastive Prompts Improve Disentanglement in Text-to-Image Diffusion Models
Text-to-image diffusion models have achieved remarkable performance in image synthesis, while the text interface does not always provide fine-grained control over certain image factors.
D3GU: Multi-Target Active Domain Adaptation via Enhancing Domain Alignment
Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques.
Personalized Face Inpainting with Diffusion Models by Parallel Visual Attention
Specifically, we insert parallel attention matrices to each cross-attention module in the denoising network, which attends to features extracted from reference images by an identity encoder.
ITI-GEN: Inclusive Text-to-Image Generation
We show that, for some attributes, images can represent concepts more expressively than text.
Towards Realistic Generative 3D Face Models
By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces.
Ranked #5 on
3D Face Reconstruction
on REALY (side-view)
PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.
Zero-shot Model Diagnosis
Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.
Semantic Image Attack for Visual Model Diagnosis
Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness.
A Latent Space of Stochastic Diffusion Models for Zero-Shot Image Editing and Guidance
We demonstrate that this latent space of stochastic diffusion models can be used in the same way as that of deterministic diffusion models in two applications.
Data-Free Class-Incremental Hand Gesture Recognition
Our sampling scheme outperforms SOTA methods significantly on two 3D skeleton gesture datasets, the publicly available SHREC 2017, and EgoGesture3D -- which we extract from a publicly available RGBD dataset.
DensePose From WiFi
Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars.
Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e. g., Gaussian) latent space of GANs, VAEs, and normalizing flows.
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e. g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses.
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face.
Ranked #2 on
3D Face Animation
on VOCASET
Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
Pixel Codec Avatars
Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances.
High-fidelity Face Tracking for AR/VR via Deep Lighting Adaptation
This paper addresses previous limitations by learning a deep learning lighting model, that in combination with a high-quality 3D face tracking algorithm, provides a method for subtle and robust facial motion transfer from a regular video to a 3D photo-realistic avatar.
3D Human Pose, Shape and Texture from Low-Resolution Images and Videos
Two common approaches to deal with low-resolution images are applying super-resolution techniques to the input, which may result in unpleasant artifacts, or simply training one model for each resolution, which is impractical in many realistic applications.
SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera
The quantitative evaluation, on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric approaches.
Expressive Telepresence via Modular Codec Avatars
It is important to note that traditional person-specific CAs are learned from few training samples, and typically lack robustness as well as limited expressiveness when transferring facial expressions.
Audio- and Gaze-driven Facial Animation of Codec Avatars
Codec Avatars are a recent class of learned, photorealistic face models that accurately represent the geometry and texture of a person in 3D (i. e., for virtual reality), and are almost indistinguishable from video.
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning
3D human shape and pose estimation from monocular images has been an active area of research in computer vision, having a substantial impact on the development of new applications, from activity recognition to creating virtual avatars.
Ranked #71 on
3D Human Pose Estimation
on MPI-INF-3DHP
Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera
Combining 3D geometric reasoning with advanced vision-based detection methods, our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%, as well as its orientation, height and depth.
Inverse Composition Discriminative Optimization for Point Cloud Registration
Rigid Point Cloud Registration (PCReg) refers to the problem of finding the rigid transformation between two sets of point clouds.
High-Resolution Deep Convolutional Generative Adversarial Networks
Generative Adversarial Networks (GANs) convergence in a high-resolution setting with a computational constrain of GPU memory capacity (from 12GB to 24 GB) has been beset with difficulty due to the known lack of convergence rate stability.
Ranked #1 on
Image Generation
on CelebA 128x128
(MS-SSIM metric)
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning
However, methods for subspace learning from subspace-valued data have been notably absent due to incompatibilities with standard problem formulations.
Discriminative Optimization: Theory and Applications to Computer Vision Problems
This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima.
Additive Component Analysis
Principal component analysis (PCA) is one of the most versatile tools for unsupervised learning with applications ranging from dimensionality reduction to exploratory data analysis and visualization.
Discriminative Optimization: Theory and Applications to Point Cloud Registration
This approach faces two main challenges: (1) designing a cost function with a local optimum at an acceptable solution, and (2) developing an efficient numerical method to search for one (or multiple) of these local optima.
Soft-Margin Mixture of Regressions
However, existing divide-and-conquer approaches fail to deal with discontinuities between partitions (e. g., Gaussian mixture of regressions) and they cannot guarantee that the partitioned input space will be homogeneously modeled by local regressors (e. g., ordinal regression).
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices.
A Functional Regression approach to Facial Landmark Tracking
This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker.
Robust end-to-end deep audiovisual speech recognition
Speech is one of the most effective ways of communication among humans.
Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection
To model temporal dependencies, Long Short-Term Memory (LSTMs) are stacked on top of these representations, regardless of the lengths of input videos.
Motion From Structure (MfS): Searching for 3D Objects in Cluttered Point Trajectories
Object detection has been a long standing problem in computer vision, and state-of-the-art approaches rely on the use of sophisticated features and/or classifiers.
Semantic Component Analysis
If weakly-supervised information is available in the form of image-level tags, SCA factorizes a set of images into semantic groups of superpixels.
Confidence Preserving Machine for Facial Action Unit Detection
Varied sources of error contribute to the challenge of facial action unit detection.
Unsupervised Synchrony Discovery in Human Interaction
We evaluate the effectiveness of our approach in multiple databases, including human actions using the CMU Mocap dataset, spontaneous facial behaviors using group-formation task dataset and parent-infant interaction dataset.
Global Supervised Descent Method
It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function.
Joint Patch and Multi-Label Learning for Facial Action Unit Detection
The most commonly used taxonomy to describe facial behaviour is the Facial Action Coding System (FACS).
Error-Correcting Factorization
To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes.
Feature and Region Selection for Visual Learning
There are four main benefits of our approach: (1) Our approach accommodates non-linear additive kernels such as the popular $\chi^2$ and intersection kernel; (2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; (3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; (4) we point out strong connections with multiple kernel learning and multiple instance learning approaches.
Complex Non-Rigid Motion 3D Reconstruction by Union of Subspaces
The task of estimating complex non-rigid 3D motion through a monocular camera is of increasing interest to the wider scientific community.
Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision
Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems.
Supervised Descent Method and Its Applications to Face Alignment
It is generally accepted that 2 nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function.
Ranked #33 on
Face Alignment
on WFLW
Deformable Graph Matching
This paper proposes deformable graph matching (DGM), an extension of GM for matching graphs subject to global rigid and non-rigid geometric constraints.
Selective Transfer Machine for Personalized Facial Action Unit Detection
To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2].
