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Found 22 papers, 12 papers with code
DreamWalk: Style Space Exploration using Diffusion Guidance
Text-conditioned diffusion models can generate impressive images, but fall short when it comes to fine-grained control.
WonderJourney: Going from Anywhere to Everywhere
We introduce WonderJourney, a modularized framework for perpetual 3D scene generation.
ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
Experiments on a wide range of subject and style combinations show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity while preserving the ability to recontextualize.
Background Prompting for Improved Object Depth
To infer object depth on a real image, we place the segmented object into the learned background prompt and run off-the-shelf depth networks.
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object.
SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow
Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations.
DynIBaR: Neural Dynamic Image-Based Rendering
Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories.
D$^2$NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video
We introduce Decoupled Dynamic Neural Radiance Field (D$^2$NeRF), a self-supervised approach that takes a monocular video and learns a 3D scene representation which decouples moving objects, including their shadows, from the static background.
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.
Learning 3D Semantic Segmentation with only 2D Image Supervision
With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.
Ranked #8 on
LIDAR Semantic Segmentation
on nuScenes
Differentiable Surface Rendering via Non-Differentiable Sampling
We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.
Consistent Depth of Moving Objects in Video
We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera.
Omnimatte: Associating Objects and Their Effects in Video
We show results on real-world videos containing interactions between different types of subjects (cars, animals, people) and complex effects, ranging from semi-transparent elements such as smoke and reflections, to fully opaque effects such as objects attached to the subject.
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images.
Layered Neural Rendering for Retiming People in Video
We present a method for retiming people in an ordinary, natural video -- manipulating and editing the time in which different motions of individuals in the video occur.
Local Deep Implicit Functions for 3D Shape
The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.
Unsupervised Learning of Object Structure and Dynamics from Videos
Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning.
Ranked #11 on
Video Prediction
on KTH
Learning the Depths of Moving People by Watching Frozen People
We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving.
Learning Shape Templates with Structured Implicit Functions
To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements.
Unsupervised Training for 3D Morphable Model Regression
We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.
Ranked #2 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter.
Synthesizing Normalized Faces from Facial Identity Features
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph.
