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Found 16 papers, 3 papers with code
Text2HOI: Text-guided 3D Motion Generation for Hand-Object Interaction
For contact generation, a VAE-based network takes as input a text and an object mesh, and generates the probability of contacts between the surfaces of hands and the object during the interaction.
3D Reconstruction of Interacting Multi-Person in Clothing from a Single Image
This paper introduces a novel pipeline to reconstruct the geometry of interacting multi-person in clothing on a globally coherent scene space from a single image.
Dynamic Appearance Modeling of Clothed 3D Human Avatars using a Single Camera
For implicit modeling, an implicit network combines the appearance and 3D motion features to decode high-fidelity clothed 3D human avatars with motion-dependent geometry and texture.
Relightful Harmonization: Lighting-aware Portrait Background Replacement
Portrait harmonization aims to composite a subject into a new background, adjusting its lighting and color to ensure harmony with the background scene.
Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation
To prove our claim, we design a novel human animation framework using a denoising diffusion model: a neural network learns to generate the image of a person by denoising temporal Gaussian noises whose intermediate results are cross-conditioned bidirectionally between consecutive frames.
Complete 3D Human Reconstruction From a Single Incomplete Image
This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e. g., a torso.
Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera
Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i. e., there exists a number of cloth geometric configurations given a pose depending on the way it has moved.
HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge
We demonstrate that HUMBI is highly effective in learning and reconstructing a complete human model and is complementary to the existing datasets of human body expressions with limited views and subjects such as MPII-Gaze, Multi-PIE, Human3. 6M, and Panoptic Studio datasets.
Neural 3D Clothes Retargeting from a Single Image
In this paper, we present a method of clothes retargeting; generating the potential poses and deformations of a given 3D clothing template model to fit onto a person in a single RGB image.
Pose-Guided Human Animation from a Single Image in the Wild
We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses.
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths from a Monocular Camera
Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents.
Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking
In this paper, we propose a self-supervised domain adaptation approach to enable the animation of high-fidelity face models from a commodity camera.
HUMBI: A Large Multiview Dataset of Human Body Expressions
This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing.
3D Semantic Trajectory Reconstruction from 3D Pixel Continuum
This paper presents a method to reconstruct dense semantic trajectory stream of human interactions in 3D from synchronized multiple videos.
VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on
Lane Detection
on Caltech Lanes Washington
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN).
Ranked #73 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
