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Found 14 papers, 2 papers with code
Pose Guided Multi-person Image Generation From Text
Transformers have recently been shown to generate high quality images from texts.
SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition
We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i. e. images of the desired illumination conditions.
Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video
We present a novel method to learn temporally consistent 3D reconstruction of clothed people from a monocular video.
Multi-person Implicit Reconstruction from a Single Image
We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image.
Multi-View Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People
This paper introduces two advances to overcome this limitation: firstly a new synthetic dataset of realistic clothed people, 3DVH; and secondly, a novel multiple-view loss function for training of monocular volumetric shape estimation, which is demonstrated to significantly improve generalisation and reconstruction accuracy.
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes
In this paper, we introduce RealMonoDepth a self-supervised monocular depth estimation approach which learns to estimate the real scene depth for a diverse range of indoor and outdoor scenes.
Learning Dense Wide Baseline Stereo Matching for People
We show that it is possible to learn stereo matching from synthetic people dataset and improve performance on real datasets for stereo reconstruction of people from narrow and wide baseline stereo data.
A*3D Dataset: Towards Autonomous Driving in Challenging Environments
With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection.
U4D: Unsupervised 4D Dynamic Scene Understanding
We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video.
Temporally Coherent General Dynamic Scene Reconstruction
Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints.
4D Temporally Coherent Light-field Video
Evaluation of the proposed light-field scene flow against existing multi-view dense correspondence approaches demonstrates a significant improvement in accuracy of temporal coherence.
Semantically Coherent Co-Segmentation and Reconstruction of Dynamic Scenes
Semantic co-segmentation exploits the coherence in semantic class labels both spatially, between views at a single time instant, and temporally, between widely spaced time instants of dynamic objects with similar shape and appearance.
Temporally coherent 4D reconstruction of complex dynamic scenes
Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects.
General Dynamic Scene Reconstruction from Multiple View Video
The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras.
