# TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Video

arXiv: https://arxiv.org/abs/2008.00158 · HTML: https://mlsanity.com/paper/2008.00158/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

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

- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [TAVA: Template-free Animatable Volumetric Actors](https://mlsanity.com/paper/2206.08929/) (2022)
- [Real-time Deep Dynamic Characters](https://mlsanity.com/paper/2105.01794/) (2021)
- [Animatable Neural Radiance Fields from Monocular RGB Videos](https://mlsanity.com/paper/2106.13629/) (2021)
- [imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose](https://mlsanity.com/paper/2108.10842/) (2021)
- [DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras](https://mlsanity.com/paper/2105.00261/) (2021)
- [Physics-based Human Motion Estimation and Synthesis from Videos](https://mlsanity.com/paper/2109.09913/) (2021)
- [Flow supervision for Deformable NeRF](https://mlsanity.com/paper/2303.16333/) (2023)
- [3D Human Texture Estimation from a Single Image with Transformers](https://mlsanity.com/paper/2109.02563/) (2021)
- [AvatarCap: Animatable Avatar Conditioned Monocular Human Volumetric Capture](https://mlsanity.com/paper/2207.02031/) (2022)
- [POSEFusion: Pose-guided Selective Fusion for Single-view Human Volumetric Capture](https://mlsanity.com/paper/2103.15331/) (2021)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [RANA: Relightable Articulated Neural Avatars](https://mlsanity.com/paper/2212.03237/) (2022)
- [Neural-ABC: Neural Parametric Models for Articulated Body with Clothes](https://mlsanity.com/paper/2404.04673/) (2024)
- [Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks](https://mlsanity.com/paper/2305.00936/) (2023)
- [AdaHuman: Animatable Detailed 3D Human Generation with Compositional Multiview Diffusion](https://mlsanity.com/paper/2505.24877/) (2025)
- [Geometry-aware Two-scale PIFu Representation for Human Reconstruction](https://mlsanity.com/paper/2112.02082/) (2021)
- [Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations](https://mlsanity.com/paper/2203.06901/) (2022)
- [DRaCoN -- Differentiable Rasterization Conditioned Neural Radiance Fields for Articulated Avatars](https://mlsanity.com/paper/2203.15798/) (2022)
- [CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction](https://mlsanity.com/paper/2207.09735/) (2022)
- [Refining 3D Human Texture Estimation from a Single Image](https://mlsanity.com/paper/2303.03471/) (2023)
- [Multimodal Active Measurement for Human Mesh Recovery in Close Proximity](https://mlsanity.com/paper/2310.08116/) (2023)
- [DeepIron: Predicting Unwarped Garment Texture from a Single Image](https://mlsanity.com/paper/2310.15447/) (2023)
- [AnthroNet: Conditional Generation of Humans via Anthropometrics](https://mlsanity.com/paper/2309.03812/) (2023)
- [On the Skinning of Gaussian Avatars](https://mlsanity.com/paper/2509.11411/) (2025)
- [VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR](https://mlsanity.com/paper/2602.01674/) (2026)
- [UVA: Towards Unified Volumetric Avatar for View Synthesis, Pose rendering, Geometry and Texture Editing](https://mlsanity.com/paper/2304.06969/) (2023)
- [FlowAvatar: Real-Time Full-Body Avatars from Sparse Egocentric Inputs on Consumer XR Devices](https://mlsanity.com/paper/2610.16277/) (2026)

## Abstract (arXiv)

We present TexMesh, a novel approach to reconstruct detailed human meshes with high-resolution full-body texture from RGB-D video. TexMesh enables high quality free-viewpoint rendering of humans. Given the RGB frames, the captured environment map, and the coarse per-frame human mesh from RGB-D tracking, our method reconstructs spatiotemporally consistent and detailed per-frame meshes along with a high-resolution albedo texture. By using the incident illumination we are able to accurately estimate local surface geometry and albedo, which allows us to further use photometric constraints to adapt a synthetically trained model to real-world sequences in a self-supervised manner for detailed surface geometry and high-resolution texture estimation. In practice, we train our models on a short example sequence for self-adaptation and the model runs at interactive framerate afterwards. We validate TexMesh on synthetic and real-world data, and show it outperforms the state of art quantitatively and qualitatively.