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Found 11 papers, 2 papers with code
NCRF: Neural Contact Radiance Fields for Free-Viewpoint Rendering of Hand-Object Interaction
Despite remarkable progress that has been achieved in this field, existing methods still fail to synthesize the hand-object interaction photo-realistically, suffering from degraded rendering quality caused by the heavy mutual occlusions between the hand and the object, and inaccurate hand-object pose estimation.
Deformable 3D Gaussian Splatting for Animatable Human Avatars
ParDy-Human introduces parameter-driven dynamics into 3D Gaussian Splatting where 3D Gaussians are deformed by a human pose model to animate the avatar.
Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video
A pose-conditioned deformable NeRF is optimized to volumetrically represent a human subject in canonical T-pose.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment
Specifically, we focus on human appearance and learn implicit pose, shape and garment representations of dressed humans from RGB images.
Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regression
Depth estimation is a core task in 3D computer vision.
Adaptive Quantization for Deep Neural Network
First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy.
On Classification of Distorted Images with Deep Convolutional Neural Networks
Image blur and image noise are common distortions during image acquisition.
Image-based Vehicle Analysis using Deep Neural Network: A Systematic Study
We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches.
