Search Results for author:
Found 42 papers, 17 papers with code
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.
Deep Snake for Real-Time Instance Segmentation
Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization.
Ranked #2 on
Semantic Contour Prediction
on Sbd val
Deep Generative Models on 3D Representations: A Survey
In this survey, we thoroughly review the ongoing developments of 3D generative models, including methods that employ 2D and 3D supervision.
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
Ranked #2 on
6D Pose Estimation using RGB
on YCB-Video
(Mean AUC metric)
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
EasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations.
Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos
Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0. 1s.
Detector-Free Structure from Motion
We propose a new detector-free SfM framework to draw benefits from the recent success of detector-free matchers to avoid the early determination of keypoints, while solving the multi-view inconsistency issue of detector-free matchers.
Neural Rays for Occlusion-aware Image-based Rendering
On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction.
Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed
Furthermore, we find spatial variance exists in LoFTR's fine correlation module, which is adverse to matching accuracy.
GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs
Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations.
Learning Neural Volumetric Representations of Dynamic Humans in Minutes
In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality.
3D-aware Image Synthesis via Learning Structural and Textural Representations
The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis.
Neural Scene Chronology
Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions.
Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score
We first use a class of penalized M-estimators for the propensity score and outcome models.
Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos.
MPS-NeRF: Generalizable 3D Human Rendering from Multiview Images
For this task, we propose a simple yet effective method to train a generalizable NeRF with multiview images as conditional input.
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis
Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects.
Ponder: Point Cloud Pre-training via Neural Rendering
We propose a novel approach to self-supervised learning of point cloud representations by differentiable neural rendering.
Learning 3D-aware Image Synthesis with Unknown Pose Distribution
Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution pre-estimated on the training set.
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet.
Grid-guided Neural Radiance Fields for Large Urban Scenes
An alternative solution is to use a feature grid representation, which is computationally efficient and can naturally scale to a large scene with increased grid resolutions.
Representing Volumetric Videos as Dynamic MLP Maps
This paper introduces a novel representation of volumetric videos for real-time view synthesis of dynamic scenes.
AutoRecon: Automated 3D Object Discovery and Reconstruction
A fully automated object reconstruction pipeline is crucial for digital content creation.
Learning Human Mesh Recovery in 3D Scenes
We present a novel method for recovering the absolute pose and shape of a human in a pre-scanned scene given a single image.
Dyn-E: Local Appearance Editing of Dynamic Neural Radiance Fields
We extensively evaluate our approach on various scenes and show that our approach achieves spatially and temporally consistent editing results.
Relightable and Animatable Neural Avatar from Sparse-View Video
Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility.
iVS-Net: Learning Human View Synthesis from Internet Videos
However, these methods usually rely on limited multi-view images typically collected in the studio or commercial high-quality 3D scans for training, which heavily prohibits their generalization capability for in-the-wild images.
Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models
This paper presents a novel approach to generating the 3D motion of a human interacting with a target object, with a focus on solving the challenge of synthesizing long-range and diverse motions, which could not be fulfilled by existing auto-regressive models or path planning-based methods.
Im4D: High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes
This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos.
4K4D: Real-Time 4D View Synthesis at 4K Resolution
Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality.
SAM-guided Graph Cut for 3D Instance Segmentation
Experimental results on the ScanNet, ScanNet++ and KITTI-360 datasets demonstrate that our method achieves robust segmentation performance and can generalize across different types of scenes.
UniDream: Unifying Diffusion Priors for Relightable Text-to-3D Generation
Recent advancements in text-to-3D generation technology have significantly advanced the conversion of textual descriptions into imaginative well-geometrical and finely textured 3D objects.
Street Gaussians for Modeling Dynamic Urban Scenes
We introduce Street Gaussians, a new explicit scene representation that tackles all these limitations.
AI Revolution on Chat Bot: Evidence from a Randomized Controlled Experiment
In recent years, generative AI has undergone major advancements, demonstrating significant promise in augmenting human productivity.
GVGEN: Text-to-3D Generation with Volumetric Representation
To simplify the generation of GaussianVolume and empower the model to generate instances with detailed 3D geometry, we propose a coarse-to-fine pipeline.
SpatialTracker: Tracking Any 2D Pixels in 3D Space
Recovering dense and long-range pixel motion in videos is a challenging problem.
IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
