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)
note the pytorch_weights.bin can be used as a drop in in the main dualpm repo for the .pth weights!
@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}, }