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Found 28 papers, 9 papers with code
OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation
Recent advances in Iterative Vision-and-Language Navigation (IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes.
Divide and Adapt: Active Domain Adaptation via Customized Learning
To accommodate active learning and domain adaption, the two naturally different tasks, in a collaborative framework, we advocate that a customized learning strategy for the target data is the key to the success of ADA solutions.
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering.
NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud
We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.
NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images
In particular, NeAT represents the 3D surface as a level set of a signed distance function (SDF) with a validity branch for estimating the surface existence probability at the query positions.
Get3DHuman: Lifting StyleGAN-Human into a 3D Generative Model using Pixel-aligned Reconstruction Priors
Specifically, we bridge the latent space of Get3DHuman with that of StyleGAN-Human via a specially-designed prior network, where the input latent code is mapped to the shape and texture feature volumes spanned by the pixel-aligned 3D reconstructor.
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels
Confidence-wise, we propose a novel sample selection strategy based on confidence representation voting instead of the widely-used small-loss criterion.
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.
Ranked #4 on
Source-Free Domain Adaptation
on VisDA-2017
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies
The key to our method is the introduction of a new sign, the NULL sign, in addition to the conventional in and out labels.
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution
To address this issue, we propose an unsupervised domain adaptation mechanism for real-world SR, named Dual ADversarial Adaptation (DADA), which only requires LR images in the target domain with available real paired data from a source camera.
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.
OctField: Hierarchical Implicit Functions for 3D Modeling
We achieve this goal by introducing a hierarchical octree structure to adaptively subdivide the 3D space according to the surface occupancy and the richness of part geometry.
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data.
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.
3DCaricShop: A Dataset and A Baseline Method for Single-view 3D Caricature Face Reconstruction
Caricature is an artistic representation that deliberately exaggerates the distinctive features of a human face to convey humor or sarcasm.
Deep Optimized Priors for 3D Shape Modeling and Reconstruction
Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples.
Intuitive, Interactive Beard and Hair Synthesis with Generative Models
We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects.
Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images
High-fidelity clothing reconstruction is the key to achieving photorealism in a wide range of applications including human digitization, virtual try-on, etc.
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.
Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks
The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification.
Ranked #3 on
3D Shape Reconstruction
on Pix3D
Learning Perspective Undistortion of Portraits
In contrast to the previous state-of-the-art approach, our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model.
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.
SiCloPe: Silhouette-Based Clothed People
The synthesized silhouettes which are the most consistent with the input segmentation are fed into a deep visual hull algorithm for robust 3D shape prediction.
Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions
Three-dimensional object recognition has recently achieved great progress thanks to the development of effective point cloud-based learning frameworks, such as PointNet and its extensions.
Deep Volumetric Video From Very Sparse Multi-View Performance Capture
We present a deep learning-based volumetric capture approach for performance capture using a passive and highly sparse multi-view capture system.
Identity Preserving Face Completion for Large Ocular Region Occlusion
Different from the state-of-the-art face inpainting methods that have no control over the synthesized content and can only handle frontal face pose, our approach can faithfully recover the missing content under various head poses while preserving the identity.
Mesoscopic Facial Geometry Inference Using Deep Neural Networks
We present a learning-based approach for synthesizing facial geometry at medium and fine scales from diffusely-lit facial texture maps.
