{"ID":2839241,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16673","arxiv_id":"2511.16673","title":"NoPo-Avatar: Generalizable and Animatable Avatars from Sparse Inputs without Human Poses","abstract":"We tackle the task of recovering an animatable 3D human avatar from a single or a sparse set of images. For this task, beyond a set of images, many prior state-of-the-art methods use accurate \"ground-truth\" camera poses and human poses as input to guide reconstruction at test-time. We show that pose-dependent reconstruction degrades results significantly if pose estimates are noisy. To overcome this, we introduce NoPo-Avatar, which reconstructs avatars solely from images, without any pose input. By removing the dependence of test-time reconstruction on human poses, NoPo-Avatar is not affected by noisy human pose estimates, making it more widely applicable. Experiments on challenging THuman2.0, XHuman, and HuGe100K data show that NoPo-Avatar outperforms existing baselines in practical settings (without ground-truth poses) and delivers comparable results in lab settings (with ground-truth poses).","short_abstract":"We tackle the task of recovering an animatable 3D human avatar from a single or a sparse set of images. For this task, beyond a set of images, many prior state-of-the-art methods use accurate \"ground-truth\" camera poses and human poses as input to guide reconstruction at test-time. We show that pose-dependent reconstru...","url_abs":"https://arxiv.org/abs/2511.16673","url_pdf":"https://arxiv.org/pdf/2511.16673v1","authors":"[\"Jing Wen\",\"Alexander G. Schwing\",\"Shenlong Wang\"]","published":"2025-11-20T18:59:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}