Reconstructing resolution-independent materials represented by procedural graphs from a coarse 3D model and a photo of an interior
3D reconstruction of garments from single in-the-wild photos
Learning to model secondary motion, e.g., clothes for a danching human, via equivariant transformations
3D reconstruction with deformable animations from casual video collections
Generating 3D avatars from VR headset cameras
3DMM as a geometry prior for implicit function representations of facial shape and texture
Automated tongue animations that match recorded speech
Monocular reconstruction in PIFu style with disentangled colors
Controllable 3D morphable face model with a coarse-to-fine structure
3D morphable face model based on neural implicit fields
An extensible library for generating 3D synthetic data with Blender that just works.
A data generation pipeline for creating semi-realistic synthetic multi-object videos for ML tasks using PyBullet and Blender.
In this paper, we propose a new assumption, generalized label shift (GLS), to improve robustness against mismatched label distributions
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