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Found 16 papers, 3 papers with code
Inverse Transport Networks
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.
News Cover Assessment via Multi-task Learning
We propose an end-to-end multi-task learning network for image clarity assessment and semantic segmentation simultaneously, the results of which can be guided for news cover assessment.
A Bayesian Inference Framework for Procedural Material Parameter Estimation
Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability.
MaterialGAN: Reflectance Capture using a Generative SVBRDF Model
We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements.
Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering
Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing problem in computer vision.
Discovering Interpretable Latent Space Directions of GANs Beyond Binary Attributes
In this way, arbitrary attributes can be edited by collecting positive data only, and the proposed method learns a controllable representation enabling manipulation of non-binary attributes like anime styles and facial characteristics.
Statistical Feature-based Personal Information Detection in Mobile Network Traffic
Since the statistical features are independent of the value and type of personal information, the trained detector is capable of identifying various types of privacy leaks and obfuscated privacy leaks.
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering
Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL.
Deformable Object Manipulation
Model-based Reinforcement Learning
+2
Neural-PBIR Reconstruction of Shape, Material, and Illumination
In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination.
Ranked #1 on
Depth Prediction
on Stanford-ORB
DELTA: Dynamic Embedding Learning with Truncated Conscious Attention for CTR Prediction
To tackle this problem, inspired by the Global Workspace Theory in conscious processing, which posits that only a specific subset of the product features are pertinent while the rest can be noisy and even detrimental to human-click behaviors, we propose a CTR model that enables Dynamic Embedding Learning with Truncated Conscious Attention for CTR prediction, termed DELTA.
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides.
A Novel Multi-Task Model Imitating Dermatologists for Accurate Differential Diagnosis of Skin Diseases in Clinical Images
Skin diseases are among the most prevalent health issues, and accurate computer-aided diagnosis methods are of importance for both dermatologists and patients.
A Hierarchical Architecture for Neural Materials
Neural reflectance models are capable of reproducing the spatially-varying appearance of many real-world materials at different scales.
RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as the ultimate filtering step to sort items for a user.
NeRF as Non-Distant Environment Emitter in Physics-based Inverse Rendering
While the environment map is commonly used as the lighting model in inverse rendering, we show that its distant lighting assumption leads to spatial invariant lighting, which can be an inaccurate approximation in real-world inverse rendering.
