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Found 18 papers, 6 papers with code
Structural Realization with GGNNs
In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree.
L4GM: Large 4D Gaussian Reconstruction Model
We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second.
Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models
We also propose a motion amplification mechanism as well as a new autoregressive synthesis scheme to generate and combine multiple 4D sequences for longer generation.
3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
However, these features are initially trained on paired text and image data, which are not optimized for 3D tasks, and often exhibit a domain gap when applied to the target data.
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i. e., videos.
Ranked #5 on
Text-to-Video Generation
on MSR-VTT
(CLIP-FID metric)
BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations
By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator.
EditGAN: High-Precision Semantic Image Editing
EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing.
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts.
Variational Amodal Object Completion
In images of complex scenes, objects are often occluding each other which makes perception tasks such as object detection and tracking, or robotic control tasks such as planning, challenging.
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.
Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.
Ranked #4 on
Single-View 3D Reconstruction
on ShapeNet
Fast Interactive Object Annotation with Curve-GCN
Our model runs at 29. 3ms in automatic, and 2. 6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.
Adversarial Contrastive Estimation
Learning by contrasting positive and negative samples is a general strategy adopted by many methods.
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
Manually labeling datasets with object masks is extremely time consuming.
Teaching Machines to Describe Images with Natural Language Feedback
Robots will eventually be part of every household.
Teaching Machines to Describe Images via Natural Language Feedback
Robots will eventually be part of every household.
