liyang
ae28d55f81
Source: RGB-D -> Dyn-HaMR -> L20 retargeting -> FoundationPose -> reference repair -> SPIDER, documented in docs/PIPELINE_LATEST.md and docs/SETUP_AND_WEIGHTS.md. Adds FoundationPose and nvdiffrast upstream snapshots, requirements/pipeline_venv.txt and the FoundationPose weight manifest/downloader. Assets (Git LFS): weights/ (WiLoR detector, HandFlow denoiser, UniDepth-L), FoundationPose checkpoints, HaMeR checkpoint, Dyn-HaMR HMP model and BMC constraints, L20 URDF/meshes, the 20260915_171525 D405 recording and the two box CADs. MANO models are not redistributed (third_party/hamer/_DATA/data/mano/README.txt). Environments, caches and run outputs excluded. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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Nvdiffrast – Modular Primitives for High-Performance Differentiable Rendering
Modular Primitives for High-Performance Differentiable Rendering
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila
http://arxiv.org/abs/2011.03277
Nvdiffrast is a PyTorch library that provides high-performance primitive operations for rasterization-based differentiable rendering.
To install:
pip install setuptools wheel ninja
pip install git+https://github.com/NVlabs/nvdiffrast.git --no-build-isolation
See ☞☞ nvdiffrast documentation ☜☜ for more information.
Licenses
Copyright © 2020–2025, NVIDIA Corporation. All rights reserved.
This work is made available under the Nvidia Source Code License.
For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing
We do not currently accept outside code contributions in the form of pull requests.
Environment map stored as part of samples/data/envphong.npz is derived from a Wave Engine
sample material
originally shared under
MIT License.
Mesh and texture stored as part of samples/data/earth.npz are derived from
3D Earth Photorealistic 2K
model originally made available under
TurboSquid 3D Model License.
Citation
@article{Laine2020diffrast,
title = {Modular Primitives for High-Performance Differentiable Rendering},
author = {Samuli Laine and Janne Hellsten and Tero Karras and Yeongho Seol and Jaakko Lehtinen and Timo Aila},
journal = {ACM Transactions on Graphics},
year = {2020},
volume = {39},
number = {6}
}