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Found 10 papers, 5 papers with code
Visual-Tactile Sensing for In-Hand Object Reconstruction
We propose a simulation environment, VT-Sim, which supports generating hand-object interaction for both rigid and deformable objects.
Freestyle Layout-to-Image Synthesis
In this work, we explore the freestyle capability of the model, i. e., how far can it generate unseen semantics (e. g., classes, attributes, and styles) onto a given layout, and call the task Freestyle LIS (FLIS).
GarmentTracking: Category-Level Garment Pose Tracking
In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface.
OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval
With the full representation of the object shape and joint states, we can address several tasks including category-level object pose estimation and the articulated object retrieval.
Region-aware Adaptive Instance Normalization for Image Harmonization
To ensure the visual style consistency between the foreground and the background, in this paper, we treat image harmonization as a style transfer problem.
Ranked #3 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
Towards Real-World Category-level Articulation Pose Estimation
This setting allows varied kinematic structures within a semantic category, and multiple instances to co-exist in an observation of real world.
Toward Fine-grained Facial Expression Manipulation
Previous methods edit an input image under the guidance of a discrete emotion label or absolute condition (e. g., facial action units) to possess the desired expression.
Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively
Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision.
