# Textured Neural Avatars

arXiv: https://arxiv.org/abs/1905.08776 · HTML: https://mlsanity.com/paper/1905.08776/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2019.

## Built on by (most cited first, 30 of 65)

- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose](https://mlsanity.com/paper/2102.06199/) (2021)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion](https://mlsanity.com/paper/2110.13746/) (2021)
- [Real-time Deep Dynamic Characters](https://mlsanity.com/paper/2105.01794/) (2021)
- [Structured Local Radiance Fields for Human Avatar Modeling](https://mlsanity.com/paper/2203.14478/) (2022)
- [EVA3D: Compositional 3D Human Generation from 2D Image Collections](https://mlsanity.com/paper/2210.04888/) (2022)
- [Animatable Neural Radiance Fields from Monocular RGB Videos](https://mlsanity.com/paper/2106.13629/) (2021)
- [Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control](https://mlsanity.com/paper/2106.02019/) (2021)
- [ASH: Animatable Gaussian Splats for Efficient and Photoreal Human Rendering](https://mlsanity.com/paper/2312.05941/) (2023)
- [AvatarReX: Real-time Expressive Full-body Avatars](https://mlsanity.com/paper/2305.04789/) (2023)
- [Driving-Signal Aware Full-Body Avatars](https://mlsanity.com/paper/2105.10441/) (2021)
- [NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions](https://mlsanity.com/paper/2212.07626/) (2022)
- [Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations](https://mlsanity.com/paper/2204.08839/) (2022)
- [Human Performance Modeling and Rendering via Neural Animated Mesh](https://mlsanity.com/paper/2209.08468/) (2022)
- [StylePeople: A Generative Model of Fullbody Human Avatars](https://mlsanity.com/paper/2104.08363/) (2021)
- [Drivable Volumetric Avatars using Texel-Aligned Features](https://mlsanity.com/paper/2207.09774/) (2022)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [UV Volumes for Real-time Rendering of Editable Free-view Human Performance](https://mlsanity.com/paper/2203.14402/) (2022)
- [DELIFFAS: Deformable Light Fields for Fast Avatar Synthesis](https://mlsanity.com/paper/2310.11449/) (2023)
- [Animatable Implicit Neural Representations for Creating Realistic Avatars from Videos](https://mlsanity.com/paper/2203.08133/) (2022)
- [Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors](https://mlsanity.com/paper/2208.11905/) (2022)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [GM-NeRF: Learning Generalizable Model-based Neural Radiance Fields from Multi-view Images](https://mlsanity.com/paper/2303.13777/) (2023)
- [Drivable Avatar Clothing: Faithful Full-Body Telepresence with Dynamic Clothing Driven by Sparse RGB-D Input](https://mlsanity.com/paper/2310.05917/) (2023)
- [Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling](https://mlsanity.com/paper/2103.14338/) (2021)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [Audio-driven Neural Gesture Reenactment with Video Motion Graphs](https://mlsanity.com/paper/2207.11524/) (2022)
- [Holoported Characters: Real-time Free-viewpoint Rendering of Humans from Sparse RGB Cameras](https://mlsanity.com/paper/2312.07423/) (2023)
- [FlexNeRF: Photorealistic Free-viewpoint Rendering of Moving Humans from Sparse Views](https://mlsanity.com/paper/2303.14368/) (2023)

## Abstract (arXiv)

We present a system for learning full-body neural avatars, i.e. deep networks that produce full-body renderings of a person for varying body pose and camera position. Our system takes the middle path between the classical graphics pipeline and the recent deep learning approaches that generate images of humans using image-to-image translation. In particular, our system estimates an explicit two-dimensional texture map of the model surface. At the same time, it abstains from explicit shape modeling in 3D. Instead, at test time, the system uses a fully-convolutional network to directly map the configuration of body feature points w.r.t. the camera to the 2D texture coordinates of individual pixels in the image frame. We show that such a system is capable of learning to generate realistic renderings while being trained on videos annotated with 3D poses and foreground masks. We also demonstrate that maintaining an explicit texture representation helps our system to achieve better generalization compared to systems that use direct image-to-image translation.