Search Results for author:
Found 7 papers, 2 papers with code
Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistency
Synthesizing images of a person in novel poses from a single image is a highly ambiguous task.
FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second
Counterintuitively, the main loss which drives FAX is on per-pixel surface normals instead of per-pixel depth, making it possible to estimate detailed body geometry without any depth supervision.
Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences
The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation.
FAUST: Dataset and Evaluation for 3D Mesh Registration
We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments.
Model-based anthropometry: Predicting measurements from 3D human scans in multiple poses
Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape.
The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models
Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion.
From Deformations to Parts: Motion-based Segmentation of 3D Objects
We develop a method for discovering the parts of an articulated object from aligned meshes capturing various three-dimensional (3D) poses.
