# LiveHand: Real-time and Photorealistic Neural Hand Rendering

arXiv: https://arxiv.org/abs/2302.07672 · HTML: https://mlsanity.com/paper/2302.07672/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2023.

## Built on by (most cited first, 26 of 26)

- [Recent Trends in 3D Reconstruction of General Non-Rigid Scenes](https://mlsanity.com/paper/2403.15064/) (2024)
- [MANUS: Markerless Grasp Capture using Articulated 3D Gaussians](https://mlsanity.com/paper/2312.02137/) (2023)
- [OHTA: One-shot Hand Avatar via Data-driven Implicit Priors](https://mlsanity.com/paper/2402.18969/) (2024)
- [Authentic Hand Avatar from a Phone Scan via Universal Hand Model](https://mlsanity.com/paper/2405.07933/) (2024)
- [BIGS: Bimanual Category-agnostic Interaction Reconstruction from Monocular Videos via 3D Gaussian Splatting](https://mlsanity.com/paper/2504.09097/) (2025)
- [BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image](https://mlsanity.com/paper/2403.08262/) (2024)
- [DyTact: Capturing Dynamic Contacts in Hand-Object Manipulation](https://mlsanity.com/paper/2506.03103/) (2025)
- [Advances in Radiance Field for Dynamic Scene: From Neural Field to Gaussian Field](https://mlsanity.com/paper/2505.10049/) (2025)
- [Dynamic EventNeRF: Reconstructing General Dynamic Scenes from Multi-view RGB and Event Streams](https://mlsanity.com/paper/2412.06770/) (2024)
- [Learning Interaction-aware 3D Gaussian Splatting for One-shot Hand Avatars](https://mlsanity.com/paper/2410.08840/) (2024)
- [JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting](https://mlsanity.com/paper/2501.19088/) (2025)
- [SRHand: Super-Resolving Hand Images and 3D Shapes via View/Pose-aware Neural Image Representations and Explicit 3D Meshes](https://mlsanity.com/paper/2509.21859/) (2025)
- [PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior](https://mlsanity.com/paper/2511.05403/) (2025)
- [Glove2Hand: Synthesizing Natural Hand-Object Interaction from Multi-Modal Sensing Gloves](https://mlsanity.com/paper/2603.20850/) (2026)
- [Towards a Robust Framework for NeRF Evaluation](https://mlsanity.com/paper/2305.18079/) (2023)
- [HiFiHR: Enhancing 3D Hand Reconstruction from a Single Image via High-Fidelity Texture](https://mlsanity.com/paper/2308.13628/) (2023)
- [XHand: Real-time Expressive Hand Avatar](https://mlsanity.com/paper/2407.21002/) (2024)
- [HandOcc: NeRF-based Hand Rendering with Occupancy Networks](https://mlsanity.com/paper/2505.02079/) (2025)
- [HandSCS: Structural Coordinate Space for Animatable Hand Gaussian Splatting](https://mlsanity.com/paper/2503.14736/) (2025)
- [NBAvatar: Neural Billboards Avatars with Realistic Hand-Face Interaction](https://mlsanity.com/paper/2603.12063/) (2026)
- [WildGHand: Learning Anti-Perturbation Gaussian Hand Avatars from Monocular In-the-Wild Videos](https://mlsanity.com/paper/2602.20556/) (2026)
- [MASS: Mesh-inellipse Aligned Deformable Surfel Splatting for Hand Reconstruction and Rendering from Egocentric Monocular Video](https://mlsanity.com/paper/2604.08943/) (2026)
- [High-Fidelity 4D Hand-Object Capture via Multi-View Spatiotemporal Tracking and Physics-Aware Gaussians](https://mlsanity.com/paper/2606.15908/) (2026)
- [Hand-4DGS: Feed-Forward 3D Gaussian Splatting for 4D Hand Reconstruction from Egocentric Videos](https://mlsanity.com/paper/2606.19156/) (2026)
- [OASIS: Occlusion-aware Single-image Hand Avatar Reconstruction via 3D Gaussian Splatting](https://mlsanity.com/paper/2607.29633/) (2026)
- [HandSplatter: Automated Digital Goniometry from Neural Rendering](https://mlsanity.com/paper/2608.09735/) (2026)

## Abstract (arXiv)

The human hand is the main medium through which we interact with our surroundings, making its digitization an important problem. While there are several works modeling the geometry of hands, little attention has been paid to capturing photo-realistic appearance. Moreover, for applications in extended reality and gaming, real-time rendering is critical. We present the first neural-implicit approach to photo-realistically render hands in real-time. This is a challenging problem as hands are textured and undergo strong articulations with pose-dependent effects. However, we show that this aim is achievable through our carefully designed method. This includes training on a low-resolution rendering of a neural radiance field, together with a 3D-consistent super-resolution module and mesh-guided sampling and space canonicalization. We demonstrate a novel application of perceptual loss on the image space, which is critical for learning details accurately. We also show a live demo where we photo-realistically render the human hand in real-time for the first time, while also modeling pose- and view-dependent appearance effects. We ablate all our design choices and show that they optimize for rendering speed and quality. Video results and our code can be accessed from https://vcai.mpi-inf.mpg.de/projects/LiveHand/