Sep 21, 2023 · In this work, we propose a novel 3D Transformer framework called LART for 3D motion transfer. With carefully-designed architectures, LART is ...

This is the PyTorch implementation of our NeurIPS 2023 paper LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer.

We propose the LART, a novel Transformer-based architecture for 3D motion transfer, carefully designed to generate fully-preserved geometric details. A novel ...

May 30, 2024 · We propose a novel 3D Transformer framework called LART for 3D motion transfer. With carefully-designed architectures, LART is able to implicitly learn the ...

Keynote in. Workshop: Learning with Tensors: Why Now and How? Computational Phenotyping using Tensor Factorization. Abstract: Chat is not available.

LART: Neural Correspondence Learning with Latent Regularization …Transformer for 3D Motion Transfer · Haoyu Chen · NeurIPS 2023 ; Degradation-Aware Unfolding Half- ...

Dec 11, 2023 · ... 45 - 12:45 LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer, Poster Session 5, Thu 10:45 - 12:

Computer Vision and Pattern Recognition(CVPR) , 2024. game. LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer

LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer · Continual Learning for Instruction Following from ...

LART: Neural Correspondence Learning with Latent Regularization Transformer for 3D Motion Transfer. In: Conference on Neural Information Processing Systems ...