From f8a42bbe39ba55c8be16477da7ae3983eb22c73e Mon Sep 17 00:00:00 2001 From: yinwanqi Date: Mon, 3 Nov 2025 14:24:41 +0800 Subject: [PATCH] update readme --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 742e25f..69215a3 100644 --- a/README.md +++ b/README.md @@ -260,6 +260,15 @@ sh slurm_test.sh {JOB_NAME} {NUM_GPU} {TRAIN_OUTPUT_DIR} {CKPT_ID} ## Citation ```text +# SMPLest-X +@article{yin2025smplest, + title={SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation}, + author={Yin, Wanqi and Cai, Zhongang and Wang, Ruisi and Zeng, Ailing and Wei, Chen and Sun, Qingping and Mei, Haiyi and Wang, Yanjun and Pang, Hui En and Zhang, Mingyuan and Zhang, Lei and Loy, Chen Change and Yamashita, Atsushi and Yang, Lei and Liu, Ziwei}, + journal={arXiv preprint arXiv:2501.09782}, + year={2025} +} + +# SMPLer-X @inproceedings{cai2023smplerx, title={{SMPLer-X}: Scaling up expressive human pose and shape estimation}, author={Cai, Zhongang and Yin, Wanqi and Zeng, Ailing and Wei, Chen and Sun, Qingping and Yanjun, Wang and Pang, Hui En and Mei, Haiyi and Zhang, Mingyuan and Zhang, Lei and Loy, Chen Change and Yang, Lei and Liu, Ziwei}, @@ -270,7 +279,7 @@ sh slurm_test.sh {JOB_NAME} {NUM_GPU} {TRAIN_OUTPUT_DIR} {CKPT_ID} ## Explore More [SMPLCap](https://github.com/SMPLCap) Projects -- [arXiv'25] [SMPLest-X](https://github.com/SMPLCap/SMPLest-X): An extended version of [SMPLer-X](https://github.com/SMPLCap/SMPLer-X) with stronger foundation models. +- [T-PAMI'25] [SMPLest-X](https://github.com/SMPLCap/SMPLest-X): An extended version of [SMPLer-X](https://github.com/SMPLCap/SMPLer-X) with stronger foundation models. - [ECCV'24] [WHAC](https://github.com/SMPLCap/WHAC): World-grounded human pose and camera estimation from monocular videos. - [CVPR'24] [AiOS](https://github.com/SMPLCap/AiOS): An all-in-one-stage pipeline combining detection and 3D human reconstruction. - [NeurIPS'23] [SMPLer-X](https://github.com/SMPLCap/SMPLer-X): Scaling up EHPS towards a family of generalist foundation models.