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Found 19 papers, 6 papers with code
Efficient 3D Implicit Head Avatar with Mesh-anchored Hash Table Blendshapes
To address these challenges, we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality.
MVDD: Multi-View Depth Diffusion Models
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
Generalizing Neural Human Fitting to Unseen Poses With Articulated SE(3) Equivariance
Learning-based methods address this but do not generalize well when the input pose is far from those seen during training.
Exemplar-based Pattern Synthesis with Implicit Periodic Field Network
In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements.
Topologically Consistent Multi-View Face Inference Using Volumetric Sampling
We propose ToFu, Topologically consistent Face from multi-view, a geometry inference framework that can produce topologically consistent meshes across facial identities and expressions using a volumetric representation instead of an explicit underlying 3DMM.
NeRD: Neural 3D Reflection Symmetry Detector
Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks.
Equivariant Point Network for 3D Point Cloud Analysis
Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies.
VaPiD: A Rapid Vanishing Point Detector via Learned Optimizers
Being able to infer 3D structures from 2D images with geometric principles, vanishing points have been a well-recognized concept in 3D vision research.
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
We present HoliCity, a city-scale 3D dataset with rich structural information.
Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
In this work, we focus on object-level 3D reconstruction and present a geometry-based end-to-end deep learning framework that first detects the mirror plane of reflection symmetry that commonly exists in man-made objects and then predicts depth maps by finding the intra-image pixel-wise correspondence of the symmetry.
Learning to Infer Implicit Surfaces without 3D Supervision
The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output.
Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning
Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation.
Ranked #1 on
3D Object Reconstruction
on ShapeNet
Soft Rasterizer: Differentiable Rendering for Unsupervised Single-View Mesh Reconstruction
We also show that our soft rasterizer can achieve comparable results to the cutting-edge supervised learning method and in various cases even better ones, especially for real-world data.
Generalized Zero-Shot Learning with Deep Calibration Network
A technical challenge of deep learning is recognizing target classes without seen data.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Perceive Your Users in Depth: Learning Universal User Representations from Multiple E-commerce Tasks
In this work, we propose to learn universal user representations across multiple tasks for more e ective personalization.
CondenseNet: An Efficient DenseNet using Learned Group Convolutions
It combines dense connectivity with a novel module called learned group convolution.
Deep Visual-Semantic Quantization for Efficient Image Retrieval
This paper presents a compact coding solution with a focus on the deep learning to quantization approach, which improves retrieval quality by end-to-end representation learning and compact encoding and has already shown the superior performance over the hashing solutions for similarity retrieval.
Cascade Ranking for Operational E-commerce Search
Real-world search applications often involve multiple factors of preferences or constraints with respect to user experience and computational costs such as search accuracy, search latency, size of search results and total CPU cost, while most existing search solutions only address one or two factors; 2).
