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Found 20 papers, 7 papers with code
GaussianBody: Clothed Human Reconstruction via 3d Gaussian Splatting
In this work, we propose a novel clothed human reconstruction method called GaussianBody, based on 3D Gaussian Splatting.
Hierarchical Fashion Design with Multi-stage Diffusion Models
Cross-modal fashion synthesis and editing offer intelligent support to fashion designers by enabling the automatic generation and local modification of design drafts. While current diffusion models demonstrate commendable stability and controllability in image synthesis, they still face significant challenges in generating fashion design from abstract design elements and fine-grained editing. Abstract sensory expressions, \eg office, business, and party, form the high-level design concepts, while measurable aspects like sleeve length, collar type, and pant length are considered the low-level attributes of clothing. Controlling and editing fashion images using lengthy text descriptions poses a difficulty. In this paper, we propose HieraFashDiff, a novel fashion design method using the shared multi-stage diffusion model encompassing high-level design concepts and low-level clothing attributes in a hierarchical structure. Specifically, we categorized the input text into different levels and fed them in different time step to the diffusion model according to the criteria of professional clothing designers. HieraFashDiff allows designers to add low-level attributes after high-level prompts for interactive editing incrementally. In addition, we design a differentiable loss function in the sampling process with a mask to keep non-edit areas. Comprehensive experiments performed on our newly conducted Hierarchical fashion dataset, demonstrate that our proposed method outperforms other state-of-the-art competitors.
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization
Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding.
Streaming Motion Forecasting for Autonomous Driving
Our benchmark inherently captures the disappearance and re-appearance of agents, presenting the emergent challenge of forecasting for occluded agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks.
Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation
To distill knowledge from a highly accurate but complex teacher model, we construct a sequence of teachers to help the student gradually adapt.
An Empirical Analysis of Range for 3D Object Detection
LiDAR-based 3D detection plays a vital role in autonomous navigation.
Multi-Modal Face Stylization with a Generative Prior
We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces.
Learning to Zoom and Unzoom
In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations.
Far3Det: Towards Far-Field 3D Detection
We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer, e. g., $>$50m.
Forecasting from LiDAR via Future Object Detection
Object detection and forecasting are fundamental components of embodied perception.
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization
To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart.
Implicit Neural Deformation for Sparse-View Face Reconstruction
In this work, we present a new method for 3D face reconstruction from sparse-view RGB images.
FOVEA: Foveated Image Magnification for Autonomous Navigation
Efficient processing of high-res video streams is safety-critical for many robotics applications such as autonomous driving.
Neighborhood-Aware Neural Architecture Search
To facilitate the application to gradient-based algorithms, we also propose a differentiable representation for the neighborhood of architectures.
Towards Streaming Perception
While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear metric to compare different methods along the Pareto optimal latency-accuracy curve.
Ranked #2 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
Photo-Sketching: Inferring Contour Drawings from Images
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
Brute-Force Facial Landmark Analysis With A 140,000-Way Classifier
While most prior work treats this as a regression problem, we instead formulate it as a discrete $K$-way classification task, where a classifier is trained to return one of $K$ discrete alignments.
Guaranteed Parameter Estimation for Discrete Energy Minimization
In this work, we propose a method to overcome this limitation through exploiting the properties of the joint problem of training time inference and learning.
Complexity of Discrete Energy Minimization Problems
Specifically, we show that general energy minimization, even in the 2-label pairwise case, and planar energy minimization with three or more labels are exp-APX-complete.
