-
- YOSHIYASU Yusuke
- National Institute of Advanced Industrial Science and Technology (AIST)
-
- ALLAIN Louise
- National Institute of Advanced Industrial Science and Technology (AIST) Paris-Saclay University
この論文をさがす
説明
<p>In this review paper, we report our model for recovering a 3D human mesh from a single 2D monocular image, called Deformable mesh transFormer (DeFormer) 1) which was published at the CVPR 2023 conference. While the current state-of-the-art models enable good performances by taking advantage of the transformer architecture to model long-range dependencies on input tokens, they suffer from a high computational cost due to the use of the standard transformer attention mechanism whose complexity is quadratic in the input sequence length. Therefore, we developed DeFormer, a human mesh recovery method that is equipped with two computationally efficient attention modules : 1) body-sparse self-attention and 2) Deformable Mesh cross-Attention (DMA). Experimental results show that DeFormer is able to efficiently leverage multi-scale feature maps and a dense mesh, which was not possible by previous transformer approaches. As a result, DeFormer achieves state-of-the-art performances on Human3.6M and 3DPW benchmarks. Code is available at https://github.com/yusukey03012/deformer.</p>
収録刊行物
-
- 日本画像学会誌
-
日本画像学会誌 62 (6), 622-632, 2023-12-10
一般社団法人 日本画像学会