TL;DR
PIXIE is a novel method that reconstructs detailed, animatable 3D whole-body avatars with realistic facial features from a single image by intelligently merging expert estimates and considering gender-specific shape variations.
Contribution
Introduces a moderator that combines body, face, and hand features based on confidence, and incorporates gender-aware shape modeling for improved 3D human reconstruction.
Findings
Outperforms state-of-the-art in shape accuracy and facial detail.
Effectively merges expert features using confidence-weighted moderation.
Accurately estimates gender-specific 3D body shapes.
Abstract
Recovering expressive humans from images is essential for understanding human behavior. Methods that estimate 3D bodies, faces, or hands have progressed significantly, yet separately. Face methods recover accurate 3D shape and geometric details, but need a tight crop and struggle with extreme views and low resolution. Whole-body methods are robust to a wide range of poses and resolutions, but provide only a rough 3D face shape without details like wrinkles. To get the best of both worlds, we introduce PIXIE, which produces animatable, whole-body 3D avatars with realistic facial detail, from a single image. For this, PIXIE uses two key observations. First, existing work combines independent estimates from body, face, and hand experts, by trusting them equally. PIXIE introduces a novel moderator that merges the features of the experts, weighted by their confidence. All part experts can…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
