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Found 42 papers, 20 papers with code
Text2Tex: Text-driven Texture Synthesis via Diffusion Models
We present Text2Tex, a novel method for generating high-quality textures for 3D meshes from the given text prompts.
3D generation on ImageNet
Existing 3D-from-2D generators are typically designed for well-curated single-category datasets, where all the objects have (approximately) the same scale, 3D location, and orientation, and the camera always points to the center of the scene.
Unsupervised Volumetric Animation
We propose a novel approach for unsupervised 3D animation of non-rigid deformable objects.
InfiniCity: Infinite-Scale City Synthesis
Toward infinite-scale 3D city synthesis, we propose a novel framework, InfiniCity, which constructs and renders an unconstrainedly large and 3D-grounded environment from random noises.
3DAvatarGAN: Bridging Domains for Personalized Editable Avatars
Finally, we propose a novel inversion method for 3D-GANs linking the latent spaces of the source and the target domains.
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects.
ScanEnts3D: Exploiting Phrase-to-3D-Object Correspondences for Improved Visio-Linguistic Models in 3D Scenes
The two popular datasets ScanRefer [16] and ReferIt3D [3] connect natural language to real-world 3D data.
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation
To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, text, partially observed shapes and combinations of these, further allowing to adjust the strength of each input.
Make-A-Story: Visual Memory Conditioned Consistent Story Generation
Our experiments for story generation on the MUGEN, the PororoSV and the FlintstonesSV dataset show that our method not only outperforms prior state-of-the-art in generating frames with high visual quality, which are consistent with the story, but also models appropriate correspondences between the characters and the background.
Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling
While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities.
Coarse-to-Fine Point Cloud Registration with SE(3)-Equivariant Representations
As global feature alignment requires the features to preserve the poses of input point clouds and local feature matching expects the features to be invariant to these poses, we propose an SE(3)-equivariant feature extractor to simultaneously generate two types of features.
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection
To achieve accurate 3D object detection at a low cost for autonomous driving, many multi-camera methods have been proposed and solved the occlusion problem of monocular approaches.
Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model
Generating images from hand-drawings is a crucial and fundamental task in content creation.
Vector Quantized Image-to-Image Translation
For example, it provides style variability for image generation and extension, and equips image-to-image translation with further extension capabilities.
Cross-Modal 3D Shape Generation and Manipulation
In this paper, we propose a generic multi-modal generative model that couples the 2D modalities and implicit 3D representations through shared latent spaces.
Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features
Recent studies show that paddings in convolutional neural networks encode absolute position information which can negatively affect the model performance for certain tasks.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning
In addition, our model can extract visual information as suggested by the text prompt, e. g., "an object in image one is moving northeast", and generate corresponding videos.
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic Segmentation
In the field of domain adaptation, a trade-off exists between the model performance and the number of target domain annotations.
InOut: Diverse Image Outpainting via GAN Inversion
Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image.
Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing
The audio-visual video parsing task aims to temporally parse a video into audio or visual event categories.
StyleGAN of All Trades: Image Manipulation with Only Pretrained StyleGAN
Recently, StyleGAN has enabled various image manipulation and editing tasks thanks to the high-quality generation and the disentangled latent space.
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation
Despite the success of deep learning on supervised point cloud semantic segmentation, obtaining large-scale point-by-point manual annotations is still a significant challenge.
InfinityGAN: Towards Infinite-Pixel Image Synthesis
We present a novel framework, InfinityGAN, for arbitrary-sized image generation.
Unsupervised Sound Localization via Iterative Contrastive Learning
Sound localization aims to find the source of the audio signal in the visual scene.
In&Out : Diverse Image Outpainting via GAN Inversion
Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image.
Unsupervised Discovery of Disentangled Manifolds in GANs
Interpretable generation process is beneficial to various image editing applications.
Continuous and Diverse Image-to-Image Translation via Signed Attribute Vectors
Generating a smooth sequence of intermediate results bridges the gap of two different domains, facilitating the morphing effect across domains.
Semantic View Synthesis
We tackle a new problem of semantic view synthesis -- generating free-viewpoint rendering of a synthesized scene using a semantic label map as input.
RetrieveGAN: Image Synthesis via Differentiable Patch Retrieval
Image generation from scene description is a cornerstone technique for the controlled generation, which is beneficial to applications such as content creation and image editing.
Controllable Image Synthesis via SegVAE
We also apply an off-the-shelf image-to-image translation model to generate realistic RGB images to better understand the quality of the synthesized semantic maps.
Large Margin Mechanism and Pseudo Query Set on Cross-Domain Few-Shot Learning
In recent years, few-shot learning problems have received a lot of attention.
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation
Few-shot classification aims to recognize novel categories with only few labeled images in each class.
Neural Design Network: Graphic Layout Generation with Constraints
The first module predicts a graph with complete relations from a graph with user-specified relations.
Dancing to Music
In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move.
Ranked #3 on
Motion Synthesis
on BRACE
Self-supervised Audio Spatialization with Correspondence Classifier
Spatial audio is an essential medium to audiences for 3D visual and auditory experience.
DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
In this work, we present an approach based on disentangled representation for generating diverse outputs without paired training images.
Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis
In this work, we propose a simple yet effective regularization term to address the mode collapse issue for cGANs.
Multimodal Unsupervised Image-To-Image Translation
Translation
Sub-GAN: An Unsupervised Generative Model via Subspaces
The recent years have witnessed significant growth in constructing robust generative models to capture informative distributions of natural data.
Diverse Image-to-Image Translation via Disentangled Representations
Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Domain Adaptation
Multimodal Unsupervised Image-To-Image Translation
+3
Unsupervised Representation Learning by Sorting Sequences
We present an unsupervised representation learning approach using videos without semantic labels.
Ranked #46 on
Self-Supervised Action Recognition
on HMDB51
Learning Structured Semantic Embeddings for Visual Recognition
First, we exploit the discriminative constraints to capture the intra- and inter-class relationships of image embeddings.
Soft-Segmentation Guided Object Motion Deblurring
To address these problems, we propose a novel model for object motion deblurring.
