Jane Wu
@janehwu.bsky.social
Postdoc @ UC Berkeley. 3D Vision/Graphics/Robotics. Prev: CS PhD @ Stanford.
janehwu.github.io
janehwu.github.io
Our method is able to reconstruct a unified human mesh from in-the-wild images, where high-frequency details like cloth wrinkles can be recovered even in the absence of any ground truth 3D data.
February 28, 2025 at 8:40 PM
Our method is able to reconstruct a unified human mesh from in-the-wild images, where high-frequency details like cloth wrinkles can be recovered even in the absence of any ground truth 3D data.
In this paper, we introduce a low-cost, optimization-based method for 3D human reconstruction guided by inferred 2D normal maps.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
February 28, 2025 at 8:40 PM
In this paper, we introduce a low-cost, optimization-based method for 3D human reconstruction guided by inferred 2D normal maps.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
I'll be presenting "Sparse-View 3D Reconstruction of Clothed Humans via Normal Maps" tomorrow morning at #WACV2025 Oral Session 1.1. Excited to share the final project of my PhD! A brief story 🧵
February 28, 2025 at 8:40 PM
I'll be presenting "Sparse-View 3D Reconstruction of Clothed Humans via Normal Maps" tomorrow morning at #WACV2025 Oral Session 1.1. Excited to share the final project of my PhD! A brief story 🧵
Our method is able to reconstruct a unified human mesh from in-the-wild images, where high-frequency details like cloth wrinkles can be recovered even in the absence of any ground truth 3D data.
February 28, 2025 at 8:35 PM
Our method is able to reconstruct a unified human mesh from in-the-wild images, where high-frequency details like cloth wrinkles can be recovered even in the absence of any ground truth 3D data.
In this paper, we introduce a low-cost, optimization-based method for 3D human reconstruction guided by inferred 2D normal maps.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
February 28, 2025 at 8:35 PM
In this paper, we introduce a low-cost, optimization-based method for 3D human reconstruction guided by inferred 2D normal maps.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.
Aiming for end-to-end differentiability, we derive analytical gradients to backpropagate from predicted normal maps to network-inferred SDF values on a tetrahedral mesh.