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Found 32 papers, 11 papers with code
WonderWorld: Interactive 3D Scene Generation from a Single Image
Existing scene generation approaches fall short of speed as they often require (1) progressively generating many views and depth maps, and (2) time-consuming optimization of the scene geometry representations.
BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation
We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K.
PhysDreamer: Physics-Based Interaction with 3D Objects via Video Generation
Unlike unconditional or text-conditioned dynamics generation, action-conditioned dynamics requires perceiving the physical material properties of objects and grounding the 3D motion prediction on these properties, such as object stiffness.
Reconstruction and Simulation of Elastic Objects with Spring-Mass 3D Gaussians
In particular, we develop and integrate a 3D Spring-Mass model into 3D Gaussian kernels, enabling the reconstruction of the visual appearance, shape, and physical dynamics of the object.
Unsupervised Discovery of Object-Centric Neural Fields
Extensive experiments show that uOCF enables unsupervised discovery of visually rich objects from a single real image, allowing applications such as 3D object segmentation and scene manipulation.
Fluid Simulation on Neural Flow Maps
We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena.
Inferring Hybrid Neural Fluid Fields from Videos
Specifically, to deal with visual ambiguities of fluid velocity, we introduce a set of physics-based losses that enforce inferring a physically plausible velocity field, which is divergence-free and drives the transport of density.
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets.
WonderJourney: Going from Anywhere to Everywhere
We introduce WonderJourney, a modularized framework for perpetual 3D scene generation.
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single Image
Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views.
Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark.
Tree-Structured Shading Decomposition
The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner.
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Learned visual dynamics models have proven effective for robotic manipulation tasks.
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition
We propose Object-Centric Neural Scattering Functions (OSFs) for learning to reconstruct object appearance from only images.
Learning Vortex Dynamics for Fluid Inference and Prediction
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video.
Accidental Light Probes
Recovering lighting in a scene from a single image is a fundamental problem in computer vision.
Differentiable Physics Simulation of Dynamics-Augmented Neural Objects
A robot can use this simulation to optimize grasps and manipulation trajectories of neural objects, or to improve the neural object models through gradient-based real-to-simulation transfer.
Unsupervised Discovery and Composition of Object Light Fields
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.
Rotationally Equivariant 3D Object Detection
To incorporate object-level rotation equivariance into 3D object detectors, we need a mechanism to extract equivariant features with local object-level spatial support while being able to model cross-object context information.
Letter-level Online Writer Identification
To make the application of writer-id more practical (e. g., on mobile devices), we focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues.
Unsupervised Discovery of Object Radiance Fields
We study the problem of inferring an object-centric scene representation from a single image, aiming to derive a representation that explains the image formation process, captures the scene's 3D nature, and is learned without supervision.
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Neural Radiance Flow for 4D View Synthesis and Video Processing
We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Weakly supervised discriminative feature learning with state information for person identification
We evaluate our model on unsupervised person re-identification and pose-invariant face recognition.
DSRGAN: Explicitly Learning Disentangled Representation of Underlying Structure and Rendering for Image Generation without Tuple Supervision
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision.
Unsupervised Person Re-identification by Soft Multilabel Learning
To overcome this problem, we propose a deep model for the soft multilabel learning for unsupervised RE-ID.
Ranked #83 on
Person Re-Identification
on DukeMTMC-reID
Unsupervised Person Re-identification by Deep Asymmetric Metric Embedding
In such a way, DECAMEL jointly learns the feature representation and the unsupervised asymmetric metric.
MIXGAN: Learning Concepts from Different Domains for Mixture Generation
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e. g., content and style) from different domains and thus generating a new domain with learned concepts.
Adversarial Attribute-Image Person Re-identification
While attributes have been widely used for person re-identification (Re-ID) which aims at matching the same person images across disjoint camera views, they are used either as extra features or for performing multi-task learning to assist the image-image matching task.
RGB-Infrared Cross-Modality Person Re-Identification
To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics.
Ranked #4 on
Cross-Modal Person Re-Identification
on SYSU-MM01
(mAP (All-search & Single-shot) metric)
Cross-Modality Person Re-identification
Cross-Modal Person Re-Identification
Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification
While metric learning is important for Person re-identification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training.
Ranked #119 on
Person Re-Identification
on Market-1501
