MOCHI enables registration-free training of multi-view 3D face reconstruction by enforcing topological consistency via a pseudo-linear inverse kinematic solver, using synthetic-data-trained 2D landmarks for alignment, and new pointmap/normal losses plus test-time optimization to outperform prior art
Unsuper- vised generative 3D shape learning from natural images
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
GeoFace generates consistent multi-view face images and 3D geometry from one input via a dual-stream diffusion framework with geometry-guided attention alignment.
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Registration-Free Learnable Multi-View Capture of Faces in Dense Semantic Correspondence
MOCHI enables registration-free training of multi-view 3D face reconstruction by enforcing topological consistency via a pseudo-linear inverse kinematic solver, using synthetic-data-trained 2D landmarks for alignment, and new pointmap/normal losses plus test-time optimization to outperform prior art
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GeoFace: Consistent Multi-View Face Generation with Geometry-Constrained Diffusion
GeoFace generates consistent multi-view face images and 3D geometry from one input via a dual-stream diffusion framework with geometry-guided attention alignment.