NPC: Neural Point Characters from Video
Shih-Yang Su, Timur Bagautdinov, Helge Rhodin

TL;DR
This paper introduces a hybrid point-based neural representation for animatable 3D human and animal models from videos, eliminating the need for expensive templates and improving generalization to new poses.
Contribution
It presents a novel point-based approach that automatically extracts 3D points and learns articulated deformations without explicit surface templates, enhancing flexibility and performance.
Findings
Outperforms prior methods on established benchmarks.
Automatically extracts points for diverse characters.
Achieves comparable results to template-based methods.
Abstract
High-fidelity human 3D models can now be learned directly from videos, typically by combining a template-based surface model with neural representations. However, obtaining a template surface requires expensive multi-view capture systems, laser scans, or strictly controlled conditions. Previous methods avoid using a template but rely on a costly or ill-posed mapping from observation to canonical space. We propose a hybrid point-based representation for reconstructing animatable characters that does not require an explicit surface model, while being generalizable to novel poses. For a given video, our method automatically produces an explicit set of 3D points representing approximate canonical geometry, and learns an articulated deformation model that produces pose-dependent point transformations. The points serve both as a scaffold for high-frequency neural features and an anchor for…
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Videos
NPC: Neural Point Characters from Video· youtube
Taxonomy
Topics3D Shape Modeling and Analysis · Human Motion and Animation · Human Pose and Action Recognition
