Jungeun Lee Sanghun Kim Han-Sol Lee Tserendorj Adiya Hwasup Lim 2024-05-22T07:01:19Z 2024-05-22T07:01:19Z 2024-05-22 2024-01-05 2472-6737 https://pubs.kist.re.kr/handle/201004/149865 https://openaccess.thecvf.com/content/WACV2024/html/Lee_PIDiffu_Pixel-Aligned_Diffusion_Model_for_High-Fidelity_Clothed_Human_Reconstruction_WACV_2024_paper.html This paper presents the Pixel-aligned Diffusion Model (PIDiffu), a new framework for reconstructing high-fidelity clothed 3D human models from a single image. While existing PIFu variants have made significant advances using more complicated 2D and 3D feature extractions, these methods still suffer from floating artifacts and body part duplication due to their reliance on point-wise occupancy field estimations. PIDiffu employs a diffusion-based strategy for line-wise estimation along the ray direction, conditioned by pixel-aligned features with a guided attention. This approach improves the local details and structural accuracy of the reconstructed body shape and is robust to unfamiliar and complex image features. Moreover, PIDiffu can be easily integrated with existing PIFu-based methods to leverage their advantages. The paper demonstrates that PIDiffu outperforms state-of-the-art methods that do not rely on parametric 3D body models. Especially, our method is superior in handling 'in-the-wild' images, such as those with complex patterned clothes unseen in the training data. English IEEE COMPUTER SOC PIDiffu: Pixel-aligned Diffusion Model for High-Fidelity Clothed Human Reconstruction Conference 10.1109/WACV57701.2024.00509 1 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp.5160 - 5169 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 5160 5169 US Waikoloa, HI 2024-01-04 2024 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION, WACV 2024 001222964605029 2-s2.0-85191987458