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Found 23 papers, 9 papers with code
DyST: Towards Dynamic Neural Scene Representations on Real-World Videos
Our Dynamic Scene Transformer (DyST) model leverages recent work in neural scene representation to learn a latent decomposition of monocular real-world videos into scene content, per-view scene dynamics, and camera pose.
DORSal: Diffusion for Object-centric Representations of Scenes et al
In this paper, we leverage recent progress in diffusion models to equip 3D scene representation learning models with the ability to render high-fidelity novel views, while retaining benefits such as object-level scene editing to a large degree.
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots
Self-supervised methods for learning object-centric representations have recently been applied successfully to various datasets.
RePAST: Relative Pose Attention Scene Representation Transformer
The Scene Representation Transformer (SRT) is a recent method to render novel views at interactive rates.
PaLM-E: An Embodied Multimodal Language Model
Large language models excel at a wide range of complex tasks.
Ranked #1 on
Visual Question Answering (VQA)
on OK-VQA
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning.
RUST: Latent Neural Scene Representations from Unposed Imagery
Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis.
Object Scene Representation Transformer
A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition.
Test-time Adaptation with Slot-Centric Models
In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.
From Variational to Deterministic Autoencoders
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
Spatio-temporal Transformer Network for Video Restoration
State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information.
Perceptual Video Super Resolution with Enhanced Temporal Consistency
Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences.
Assessing Generative Models via Precision and Recall
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.
Tempered Adversarial Networks
A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.
Frame-Recurrent Video Super-Resolution
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.
Ranked #6 on
Video Super-Resolution
on MSU Video Upscalers: Quality Enhancement
(VMAF metric)
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input.
Depth Estimation Through a Generative Model of Light Field Synthesis
Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks.
Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs).
