benkaye/dualpm

model_paths: (dualpm-full/dualpm-benchmark)

usage:

root_path = "benkaye/dualpm"
variant = "dualpm-full" # or "dualpm-benchmark"
model_path = f"{root_path}/{variant}"

config = DualPMConfig.from_pretrained(model_path)
model = DualPMModel.from_pretrained(model_path, config=config)

# pointclouds
# IF return_pointclouds=True,  per batch list of [Nx3] [Nx3],[N], (B H W D tensor)
# IF False, tensors (B H W D 3), (B H W D 3), (B H W D),  (B H W D)
rec, canon, conf, occ = model.infer(feat, mask, return_pointclouds=True)

@misc{kaye2025dualpmdualposedcanonicalpoint, title={DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction}, author={Ben Kaye and Tomas Jakab and Shangzhe Wu and Christian Rupprecht and Andrea Vedaldi}, year={2025}, eprint={2412.04464}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.04464}, }

BY Nvidia / SongUNet backbone @misc{karras2022elucidatingdesignspacediffusionbased, title={Elucidating the Design Space of Diffusion-Based Generative Models}, author={Tero Karras and Miika Aittala and Timo Aila and Samuli Laine}, year={2022}, eprint={2206.00364}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2206.00364}, }