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Found 163 papers, 84 papers with code
Cross-Domain Cascaded Deep Translation
We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features.
TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics.
Piece it Together: Part-Based Concepting with IP-Priors
Advanced generative models excel at synthesizing images but often rely on text-based conditioning.
Tight Inversion: Image-Conditioned Inversion for Real Image Editing
This process has an inherent tradeoff between reconstruction and editability, limiting the editing of challenging images such as highly-detailed ones.
AnyTop: Character Animation Diffusion with Any Topology
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular nature of the data.
Dynamic Concepts Personalization from Single Videos
In this paper, we introduce Set-and-Sequence, a novel framework for personalizing Diffusion Transformers (DiTs)-based generative video models with dynamic concepts.
IP-Composer: Semantic Composition of Visual Concepts
Through comprehensive evaluation, we show that our approach enables more precise control over a larger range of visual concept compositions.
SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation
In this work, we introduce SwiftSketch, a diffusion model for image-conditioned vector sketch generation that can produce high-quality sketches in less than a second.
NeuralSVG: An Implicit Representation for Text-to-Vector Generation
We additionally demonstrate that utilizing a neural representation provides an added benefit of inference-time control, enabling users to dynamically adapt the generated SVG based on user-provided inputs, all with a single learned representation.
Nested Attention: Semantic-aware Attention Values for Concept Personalization
In this work, we introduce Nested Attention, a novel mechanism that injects a rich and expressive image representation into the model's existing cross-attention layers.
Object-level Visual Prompts for Compositional Image Generation
A key challenge in this task is to preserve the identity of the objects depicted in the input visual prompts, while also generating diverse compositions across different images.
Omni-ID: Holistic Identity Representation Designed for Generative Tasks
Our approach uses a few-to-many identity reconstruction training paradigm, where a limited set of input images is used to reconstruct multiple target images of the same individual in various poses and expressions.
Stable Flow: Vital Layers for Training-Free Image Editing
The main challenge is that, unlike the UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it unclear in which layers to perform the injection.
ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation
Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows.
Object-level Scene Deocclusion
Deoccluding the hidden portions of objects in a scene is a formidable task, particularly when addressing real-world scenes.
Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing?
Layered Image Vectorization via Semantic Simplification
This work presents a progressive image vectorization technique that reconstructs the raster image as layer-wise vectors from semantic-aligned macro structures to finer details.
Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning
We design a neural network to predict the Voronoi diagram from an input point cloud or distance field via a binary classification.
pOps: Photo-Inspired Diffusion Operators
To harness this potential, we introduce pOps, a framework that trains specific semantic operators directly on CLIP image embeddings.
EmoEdit: Evoking Emotions through Image Manipulation
Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotional responses.
Lazy Diffusion Transformer for Interactive Image Editing
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
Dynamic Typography: Bringing Text to Life via Video Diffusion Prior
Through quantitative and qualitative evaluations, we demonstrate the effectiveness of our framework in generating coherent text animations that faithfully interpret user prompts while maintaining readability.
LCM-Lookahead for Encoder-based Text-to-Image Personalization
In this work, we explore the potential of using such shortcut-mechanisms to guide the personalization of text-to-image models to specific facial identities.
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation
Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images.
MyVLM: Personalizing VLMs for User-Specific Queries
To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image.
Implicit Style-Content Separation using B-LoRA
In this paper, we introduce B-LoRA, a method that leverages LoRA (Low-Rank Adaptation) to implicitly separate the style and content components of a single image, facilitating various image stylization tasks.
ReNoise: Real Image Inversion Through Iterative Noising
However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model.
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.
PALP: Prompt Aligned Personalization of Text-to-Image Models
We term our approach prompt-aligned personalization.
Generating Non-Stationary Textures using Self-Rectification
This paper addresses the challenge of example-based non-stationary texture synthesis.
SAI3D: Segment Any Instance in 3D Scenes
Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories.
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment.
Style Aligned Image Generation via Shared Attention
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts.
Curved Diffusion: A Generative Model With Optical Geometry Control
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth.
CLiC: Concept Learning in Context
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern.
Breathing Life Into Sketches Using Text-to-Video Priors
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually.
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity.
Cross-Image Attention for Zero-Shot Appearance Transfer
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images.
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
This paper presents a new text-guided technique for generating 3D shapes.
Noise-Free Score Distillation
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains.
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion
We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos.
ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints
Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery.
EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
Most of the existing work in this area focuses on feature design, while little attention has been paid to dataset construction.
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts.
Facial Reenactment Through a Personalized Generator
In this paper, we propose a novel method for facial reenactment using a personalized generator.
SVNR: Spatially-variant Noise Removal with Denoising Diffusion
Our formulation also accounts for the correlation that exists between the condition image and the samples along the modified diffusion process.
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature.
Concept Decomposition for Visual Exploration and Inspiration
Each node in the tree represents a sub-concept using a learned vector embedding injected into the latent space of a pretrained text-to-image model.
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts.
A Neural Space-Time Representation for Text-to-Image Personalization
We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder.
Delta Denoising Score
We introduce Delta Denoising Score (DDS), a novel scoring function for text-based image editing that guides minimal modifications of an input image towards the content described in a target prompt.
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
We show that using proxies allows a wide variety of editing options, such as adjusting the placement of each independent object, removing objects from a scene, or refining an object.
SKED: Sketch-guided Text-based 3D Editing
To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance.
P+: Extended Textual Conditioning in Text-to-Image Generation
Furthermore, we utilize the unique properties of this space to achieve previously unattainable results in object-style mixing using text-to-image models.
Word-As-Image for Semantic Typography
A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability.
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Specifically, we employ two components: First, an encoder that takes as an input a single image of a target concept from a given domain, e. g. a specific face, and learns to map it into a word-embedding representing the concept.
Cross-domain Compositing with Pretrained Diffusion Models
Diffusion models have enabled high-quality, conditional image editing capabilities.
Single Motion Diffusion
We harness the power of diffusion models and present a denoising network explicitly designed for the task of learning from a single input motion.
TEXTure: Text-Guided Texturing of 3D Shapes
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes.
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
Domain Expansion of Image Generators
First, we note the generator contains a meaningful, pretrained latent space.
CLIPascene: Scene Sketching with Different Types and Levels of Abstraction
In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction.
Sketch-Guided Text-to-Image Diffusion Models
In this work, we introduce a universal approach to guide a pretrained text-to-image diffusion model, with a spatial map from another domain (e. g., sketch) during inference time.
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Our Null-text inversion, based on the publicly available Stable Diffusion model, is extensively evaluated on a variety of images and prompt editing, showing high-fidelity editing of real images.
Ranked #6 on
Text-based Image Editing
on PIE-Bench
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
This unique combination of text and shape guidance allows for increased control over the generation process.
Ranked #3 on
Text to 3D
on T$^3$Bench
Human Motion Diffusion Model
In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for the human motion domain.
Ranked #1 on
Motion Synthesis
on HumanAct12
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
Ranked #7 on
Personalized Image Generation
on DreamBooth
Prompt-to-Prompt Image Editing with Cross Attention Control
Editing is challenging for these generative models, since an innate property of an editing technique is to preserve most of the original image, while in the text-based models, even a small modification of the text prompt often leads to a completely different outcome.
Ranked #17 on
Text-based Image Editing
on PIE-Bench
MoDi: Unconditional Motion Synthesis from Diverse Data
In this work, we present MoDi -- a generative model trained in an unsupervised setting from an extremely diverse, unstructured and unlabeled dataset.
Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons
We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose.
MyStyle: A Personalized Generative Prior
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
MotionCLIP: Exposing Human Motion Generation to CLIP Space
MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP) model.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
Self-Distilled StyleGAN: Towards Generation from Internet Photos
To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.
Multi-level Latent Space Structuring for Generative Control
Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity.
CLIPasso: Semantically-Aware Object Sketching
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings.
Self-Conditioned Generative Adversarial Networks for Image Editing
We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations.
FEAT: Face Editing with Attention
In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.
SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.
Third Time's the Charm? Image and Video Editing with StyleGAN3
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
ShapeFormer: Transformer-based Shape Completion via Sparse Representation
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds.
Stitch it in Time: GAN-Based Facial Editing of Real Videos
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.
NeuralMLS: Geometry-Aware Control Point Deformation
In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks.
Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure
In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre.
Mesh Draping: Parametrization-Free Neural Mesh Transfer
The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh.
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?
LARGE: Latent-Based Regression through GAN Semantics
For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner.
StyleFusion: A Generative Model for Disentangling Spatial Segments
Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes.
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.
Pivotal Tuning for Latent-based Editing of Real Images
The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance.
MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras
In this paper, we introduce Neural Motion Consensus Flow (MoCo-Flow), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time-variant function.
Consistent Two-Flow Network for Tele-Registration of Point Clouds
In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration.
Z2P: Instant Visualization of Point Clouds
We present a technique for visualizing point clouds using a neural network.
FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction
We compare our model to state-of-the-art methods that are not ep-free and show that in the absence of camera parameters, we outperform them by a large margin while obtaining comparable results when camera parameters are available.
Ranked #23 on
3D Human Pose Estimation
on Human3.6M
Orienting Point Clouds with Dipole Propagation
In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands.
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
Clusterplot: High-dimensional Cluster Visualization
We present Clusterplot, a multi-class high-dimensional data visualization tool designed to visualize cluster-level information offering an intuitive understanding of the cluster inter-relations.
Data Visualization
Graphics
SWAGAN: A Style-based Wavelet-driven Generative Model
In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs).
Ranked #11 on
Image Generation
on FFHQ 1024 x 1024
Designing an Encoder for StyleGAN Image Manipulation
We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control over the generated image.
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.
Neural Alignment for Face De-pixelization
Although simple, our framework synthesizes high-quality face reconstructions, demonstrating that given the statistical prior of a human face, multiple aligned pixelated frames contain sufficient information to reconstruct a high-quality approximation of the original signal.
SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis
The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details.
Object Properties Inferring from and Transfer for Human Interaction Motions
In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.
Self-Sampling for Neural Point Cloud Consolidation
We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement
Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind.
Deep Geometric Texture Synthesis
Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
Towards a Neural Graphics Pipeline for Controllable Image Generation
NGP decomposes the image into a set of interpretable appearance feature maps, uncovering direct control handles for controllable image generation.
Point2Mesh: A Self-Prior for Deformable Meshes
We optimize the network weights to deform an initial mesh to shrink-wrap a single input point cloud.
Face Identity Disentanglement via Latent Space Mapping
Learning disentangled representations of data is a fundamental problem in artificial intelligence.
Unpaired Motion Style Transfer from Video to Animation
In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.
Skeleton-Aware Networks for Deep Motion Retargeting
In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.
Image Morphing with Perceptual Constraints and STN Alignment
In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances.
Single Pair Cross-Modality Super Resolution
The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.
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.
PointGMM: a Neural GMM Network for Point Clouds
We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud.
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.
GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation
Instead of executing translation directly, we steer the translation by requiring the network to produce in-between images that resemble weighted hybrids between images from the input domains.
Unsupervised multi-modal Styled Content Generation
The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D.
PU-GAN: a Point Cloud Upsampling Adversarial Network
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.
Cross-Domain Cascaded Deep Feature Translation
Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones.
Learning Character-Agnostic Motion for Motion Retargeting in 2D
In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.
Unsupervised Detection of Distinctive Regions on 3D Shapes
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.
Image Resizing by Reconstruction from Deep Features
In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information.
Implicit Pairs for Boosting Unpaired Image-to-Image Translation
As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets.
Blind Visual Motif Removal from a Single Image
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer
The long-coveted task of reconstructing 3D geometry from images is still a standing problem.
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation.
CrossNet: Latent Cross-Consistency for Unpaired Image Translation
Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation.
Global-to-Local Generative Model for 3D Shapes
We also introduce new means to measure and evaluate the performance of an adversarial generative model.
Deep motifs and motion signatures
In this paper we introduce motion motifs and motion signatures that are a succinct but descriptive representation of motion sequences.
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling.
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks.
Generative Low-Shot Network Expansion
We present a simple yet powerful hard distillation method where the base network is augmented with additional weights to classify the novel classes, while keeping the weights of the base network unchanged.
MeshCNN: A Network with an Edge
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.
Multi-Scale Context Intertwining for Semantic Segmentation
Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context.
Deep Video-Based Performance Cloning
After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances.
Structure-aware Generative Network for 3D-Shape Modeling
The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.
Graphics
GRAINS: Generative Recursive Autoencoders for INdoor Scenes
We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.
Graphics
EC-Net: an Edge-aware Point set Consolidation Network
In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.
Non-Stationary Texture Synthesis by Adversarial Expansion
We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar.
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications.
ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning
The process of aligning a pair of shapes is a fundamental operation in computer graphics.
P2P-NET: Bidirectional Point Displacement Net for Shape Transform
We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e. g., meso-skeletons and surfaces, partial and complete scans, etc.
Clustering-driven Deep Embedding with Pairwise Constraints
In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.
Outlier Detection for Robust Multi-dimensional Scaling
Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization.
PU-Net: Point Cloud Upsampling Network
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.
Ranked #3 on
Point Cloud Super Resolution
on SHREC15
Neuron-level Selective Context Aggregation for Scene Segmentation
Contextual information provides important cues for disambiguating visually similar pixels in scene segmentation.
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common "scene-resolution".
Ranked #79 on
Semantic Segmentation
on NYU Depth v2
Bundle Optimization for Multi-aspect Embedding
Understanding semantic similarity among images is the core of a wide range of computer vision applications.
Co-segmentation for Space-Time Co-located Collections
Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.
A Holistic Approach for Data-Driven Object Cutout
We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask.
Synthesizing Training Images for Boosting Human 3D Pose Estimation
Human 3D pose estimation from a single image is a challenging task with numerous applications.
Spherical Embedding of Inlier Silhouette Dissimilarities
In this paper, we introduce a spherical embedding technique to position a given set of silhouettes of an object as observed from a set of cameras arbitrarily positioned around the object.
Constraints as Features
In this paper, we introduce a new approach to constrained clustering which treats the constraints as features.
