Rastering algorithm to approximate the rendering of a 3D model silhouette in a fully differentiable way.
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Updated
Dec 22, 2018 - Python
Rastering algorithm to approximate the rendering of a 3D model silhouette in a fully differentiable way.
Differentiable Renderer
TF Mesh Renderer
Differentiable point cloud rendering
Supplemental material of the article "Reparameterizing discontinuous integrands for differentiable rendering"
A differentiable renderer written purely in TensorFlow.
Differentiable Rendering Optimization using image masks.
SIGGRAPH Asia 2020: Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects
Official code for "End-to-End Optimization of Scene Layout" -- including VAE, Diff Render, SPADE for colorization (CVPR 2020 Oral)
[cvpr 20] Demo, training and evaluation code for joint hand-object pose estimation in sparsely annotated videos
[CVPR 2021 - Oral] UnsupervisedR&R: Unsupervised Point Cloud Registration via Differentiable Rendering
Code for DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss (ECCVW 2020)
Escaping Plato’s Cave: 3D Shape from Adversarial Rendering [ICCV 2019]
TensorFlow implementation of our CVPR 2021 Paper "Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes".
This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"
Project Page of 'GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction' [CVPR2019]
Project page of paper "Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning"
Applications of the Teg differentiable programming language to problems spanning graphics and physical simulation.
This repository contains reimplementation of existing neural renderer based 2D-to-3D mesh editing and texture mapping approach using the latest libraries like Pytorch etc.
PyTorch implementation of the human neural rendering in unseen positions presented at WACV 2022 "Creating and Reenacting Controllable 3D Humans with Differentiable Rendering"
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