# Relightable and Animatable Neural Avatar from Sparse-View Video

arXiv: https://arxiv.org/abs/2308.07903 · HTML: https://mlsanity.com/paper/2308.07903/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 83 references that are in the map.
- First posted on arXiv 15 Aug 2023.

## Builds on (most cited first, 30 of 39)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [NeRD: Neural Reflectance Decomposition from Image Collections](https://mlsanity.com/paper/2012.03918/) (2020)
- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting](https://mlsanity.com/paper/2104.00674/) (2021)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [NeuMan: Neural Human Radiance Field from a Single Video](https://mlsanity.com/paper/2203.12575/) (2022)
- [Video Based Reconstruction of 3D People Models](https://mlsanity.com/paper/1803.04758/) (2018)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition](https://mlsanity.com/paper/2110.14373/) (2021)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (2021)
- [Modeling Indirect Illumination for Inverse Rendering](https://mlsanity.com/paper/2204.06837/) (2022)
- [DeepCap: Monocular Human Performance Capture Using Weak Supervision](https://mlsanity.com/paper/2003.08325/) (2020)
- [H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion](https://mlsanity.com/paper/2110.13746/) (2021)
- [ARAH: Animatable Volume Rendering of Articulated Human SDFs](https://mlsanity.com/paper/2210.10036/) (2022)
- [Robust High-Resolution Video Matting with Temporal Guidance](https://mlsanity.com/paper/2108.11515/) (2021)
- [Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing](https://mlsanity.com/paper/2204.08906/) (2022)
- [Structured Local Radiance Fields for Human Avatar Modeling](https://mlsanity.com/paper/2203.14478/) (2022)
- [LiveCap: Real-time Human Performance Capture from Monocular Video](https://mlsanity.com/paper/1810.02648/) (2018)
- [SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections](https://mlsanity.com/paper/2205.15768/) (2022)
- [Neural Light Transport for Relighting and View Synthesis](https://mlsanity.com/paper/2008.03806/) (2020)
- [Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2209.10510/) (2022)
- [MonoPerfCap: Human Performance Capture from Monocular Video](https://mlsanity.com/paper/1708.02136/) (2017)
- [Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera](https://mlsanity.com/paper/2206.15258/) (2022)

## Built on by (most cited first, 28 of 28)

- [MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos](https://mlsanity.com/paper/2407.08414/) (2024)
- [Animatable and Relightable Gaussians for High-fidelity Human Avatar Modeling](https://mlsanity.com/paper/2311.16096/) (2023)
- [Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion](https://mlsanity.com/paper/2406.09402/) (2024)
- [IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing](https://mlsanity.com/paper/2312.05210/) (2023)
- [Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models](https://mlsanity.com/paper/2507.13344/) (2025)
- [Gaussian Shadow Casting for Neural Characters](https://mlsanity.com/paper/2401.06116/) (2024)
- [Surfel-based Gaussian Inverse Rendering for Fast and Relightable Dynamic Human Reconstruction from Monocular Video](https://mlsanity.com/paper/2407.15212/) (2024)
- [RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images](https://mlsanity.com/paper/2503.14198/) (2025)
- [RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians](https://mlsanity.com/paper/2501.07104/) (2025)
- [HumanOLAT: A Large-Scale Dataset for Full-Body Human Relighting and Novel-View Synthesis](https://mlsanity.com/paper/2508.09137/) (2025)
- [Interactive Rendering of Relightable and Animatable Gaussian Avatars](https://mlsanity.com/paper/2407.10707/) (2024)
- [BEAM: Bridging Physically-based Rendering and Gaussian Modeling for Relightable Volumetric Video](https://mlsanity.com/paper/2502.08297/) (2025)
- [Choreographing a World of Dynamic Objects](https://mlsanity.com/paper/2601.04194/) (2026)
- [Generalizable and Relightable Gaussian Splatting for Human Novel View Synthesis](https://mlsanity.com/paper/2505.21502/) (2025)
- [NECA: Neural Customizable Human Avatar](https://mlsanity.com/paper/2403.10335/) (2024)
- [WildAvatar: Learning In-the-wild 3D Avatars from the Web](https://mlsanity.com/paper/2407.02165/) (2024)
- [Bringing Your Portrait to 3D Presence](https://mlsanity.com/paper/2511.22553/) (2025)
- [DNF-Avatar: Distilling Neural Fields for Real-time Animatable Avatar Relighting](https://mlsanity.com/paper/2504.10486/) (2025)
- [Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views](https://mlsanity.com/paper/2512.00255/) (2025)
- [FastAnimate: Towards Learnable Template Construction and Pose Deformation for Fast 3D Human Avatar Animation](https://mlsanity.com/paper/2512.01444/) (2025)
- [Relightable and Dynamic Gaussian Avatar Reconstruction from Monocular Video](https://mlsanity.com/paper/2512.09335/) (2025)
- [HumanMaterial: Human Material Estimation from a Single Image via Progressive Training](https://mlsanity.com/paper/2507.18385/) (2025)
- [HR Human: Modeling Human Avatars with Triangular Mesh and High-Resolution Textures from Videos](https://mlsanity.com/paper/2405.11270/) (2024)
- [Physically Plausible Human-Object Rendering from Sparse Views via 3D Gaussian Splatting](https://mlsanity.com/paper/2503.09640/) (2025)
- [Fast and Physically-based Neural Explicit Surface for Relightable Human Avatars](https://mlsanity.com/paper/2503.18408/) (2025)
- [HarmoVid: Relightful Video Portrait Harmonization](https://mlsanity.com/paper/2605.28811/) (2026)
- [Generative Relightable Avatars](https://mlsanity.com/paper/2606.22718/) (2026)
- [Relightable 3D Avatar Reconstruction with Semantic-Adaptive Motion-Illumination Responses](https://mlsanity.com/paper/2609.24158/) (2026)

## Abstract (arXiv)

This paper tackles the challenge of creating relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination. Compared to studio environments, this setting is more practical and accessible but poses an extremely challenging ill-posed problem. Previous neural human reconstruction methods are able to reconstruct animatable avatars from sparse views using deformed Signed Distance Fields (SDF) but cannot recover material parameters for relighting. While differentiable inverse rendering-based methods have succeeded in material recovery of static objects, it is not straightforward to extend them to dynamic humans as it is computationally intensive to compute pixel-surface intersection and light visibility on deformed SDFs for inverse rendering. To solve this challenge, we propose a Hierarchical Distance Query (HDQ) algorithm to approximate the world space distances under arbitrary human poses. Specifically, we estimate coarse distances based on a parametric human model and compute fine distances by exploiting the local deformation invariance of SDF. Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility. This allows us to develop the first system to recover animatable and relightable neural avatars from sparse view (or monocular) inputs. Experiments demonstrate that our approach is able to produce superior results compared to state-of-the-art methods. Our code will be released for reproducibility.