FLASHand: Feed-forward reLightable and animatable aingle-view hand reconstruction

Zhang, Ling-Xiao, Gao, Lin, He, Wei-Hong, Yang, Yu-Xuan, Xing, Yunbing, Lai, Yu-Kun ORCID: https://orcid.org/0000-0002-2094-5680 and Chen, Yiqiang 2026. FLASHand: Feed-forward reLightable and animatable aingle-view hand reconstruction. Presented at: SIGGRAPH 2026, Los Angeles, CA, USA, 19-23 July 2026. SIGGRAPH Conference Papers '26: Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers. (43) ACM, 10.1145/3799902.3811039

PDF - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (96MB)

Abstract

Reconstructing high-fidelity, animatable, and relightable 3D hand avatars from a single RGB image is a challenging yet critical task for immersive VR/AR applications. State-of-the-art hand reconstruction methods achieve impressive reconstruction and relighting results, but they mostly leverage dense observations, such as multi-view images or monocular video sequences and rely on per-scene optimization. Moreover, it is difficult for these methods to generate plausible appearance in occluded regions. In contrast, existing single-view hand reconstruction methods typically struggle to disentangle global illumination, resulting in textures with baked-in shading artifacts. To address these challenges, we propose FLASHand, the first feed-forward relightable and animatable 3D hand reconstruction model from a single RGB image. Our method bridges the gap between efficiency and fidelity, enabling instant creation of personalized hand avatars with disentangled appearance that can be rendered under novel lighting and poses. To ensure plausible geometry generation and achieve high-fidelity animation and relighting, we introduce a mesh-based disentangled 2D Gaussian splatting hand representation. We leverage the NIMBLE parametric prior to define a canonical attribute space, and define geometry and appearance attributes explicitly. We then propose the Feed-forward Hand Attributes Generator (FHAG) to predict these attributes from a single image. By leveraging a cross attention module to fuse canonical geometric information with input image features, FHAG effectively lifts pixel-level visual cues into the 3D hand canonical space, directly generating spatially-aligned geometry and appearance attributes. This allows us to bypass iterative optimization and instantly reconstruct a high-fidelity hand avatar from a single RGB image. Extensive experiments on both synthetic and in-the-wild datasets demonstrate that FLASHand achieves state-of-the-art performance in novel view synthesis and supports real-time animation and relighting. Code and data are available at https://github.com/IGLICT/FLASHand.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	ACM
ISBN:	979-8-4007-2554-8
Date of First Compliant Deposit:	21 July 2026
Last Modified:	21 Jul 2026 11:16
URI:	https://orca.cardiff.ac.uk/id/eprint/188380

Actions (repository staff only)

Edit Item