Search Results for author:
Found 23 papers, 7 papers with code
Cosmos World Foundation Model Platform for Physical AI
We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications.
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
Our method is a generalizable feedforward model that predicts a rich neural scene representation from sparse, single-frame multi-view camera inputs with limited view overlap, and is trained self-supervised with differentiable rendering to reconstruct RGB, depth, or feature images.
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.
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content.
EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models
Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks.
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.
WildFusion: Learning 3D-Aware Latent Diffusion Models in View Space
Then, we train a diffusion model in the 3D-aware latent space, thereby enabling synthesis of high-quality 3D-consistent image samples, outperforming recent state-of-the-art GAN-based methods.
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes.
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.
NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models
We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene.
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)
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps
Modern image generative models show remarkable sample quality when trained on a single domain or class of objects.
Generative Adversarial Network
Image-to-Image Translation
+1
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.
Visual TransforMatcher: Efficient Match-to-Match Attention for Visual Correspondence
Establishing correspondences between images remains a challenging task, especially under large appearance changes due to different viewpoints and intra-class variations.
DriveGAN: Towards a Controllable High-Quality Neural Simulation
Realistic simulators are critical for training and verifying robotics systems.
Self-supervised driven consistency training for annotation efficient histopathology image analysis
In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data.
Histopathological Image Classification
Representation Learning
+1
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.
Edge Network-Assisted Real-Time Object Detection Framework for Autonomous Driving
In an EODF, AVs extract the region of interests~(RoIs) of the captured image when the channel quality is not sufficiently good for supporting real-time OD.
Learning to Simulate Dynamic Environments with GameGAN
Simulation is a crucial component of any robotic system.
The Shmoop Corpus: A Dataset of Stories with Loosely Aligned Summaries
Understanding stories is a challenging reading comprehension problem for machines as it requires reading a large volume of text and following long-range dependencies.
Cascaded Pyramid Network for 3D Human Pose Estimation Challenge
Over the past decade, there has been a growing interest in human pose estimation.
Visual Reasoning by Progressive Module Networks
Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output.
