# Implicit Feature Networks for Texture Completion from Partial 3D Data

arXiv: https://arxiv.org/abs/2009.09458 · HTML: https://mlsanity.com/paper/2009.09458/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 30 of 31)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose](https://mlsanity.com/paper/2108.10842/) (2021)
- [Towards Implicit Text-Guided 3D Shape Generation](https://mlsanity.com/paper/2203.14622/) (2022)
- [Texturify: Generating Textures on 3D Shape Surfaces](https://mlsanity.com/paper/2204.02411/) (2022)
- [AutoRF: Learning 3D Object Radiance Fields from Single View Observations](https://mlsanity.com/paper/2204.03593/) (2022)
- [NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering](https://mlsanity.com/paper/2304.10080/) (2023)
- [Learned Vertex Descent: A New Direction for 3D Human Model Fitting](https://mlsanity.com/paper/2205.06254/) (2022)
- [JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction](https://mlsanity.com/paper/2204.10549/) (2022)
- [Intrinsic Neural Fields: Learning Functions on Manifolds](https://mlsanity.com/paper/2203.07967/) (2022)
- [Unsupervised Occupancy Learning from Sparse Point Cloud](https://mlsanity.com/paper/2404.02759/) (2024)
- [A Survey on 3D Human Avatar Modeling -- From Reconstruction to Generation](https://mlsanity.com/paper/2406.04253/) (2024)
- [Mixing-Denoising Generalizable Occupancy Networks](https://mlsanity.com/paper/2311.12125/) (2023)
- [NeAT: Neural Adaptive Tomography](https://mlsanity.com/paper/2202.02171/) (2022)
- [Robustifying Generalizable Implicit Shape Networks with a Tunable Non-Parametric Model](https://mlsanity.com/paper/2311.12967/) (2023)
- [SCoDA: Domain Adaptive Shape Completion for Real Scans](https://mlsanity.com/paper/2304.10179/) (2023)
- [LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation](https://mlsanity.com/paper/2103.14273/) (2021)
- [TemporalUV: Capturing Loose Clothing with Temporally Coherent UV Coordinates](https://mlsanity.com/paper/2204.03671/) (2022)
- [Toward Robust Neural Reconstruction from Sparse Point Sets](https://mlsanity.com/paper/2412.16361/) (2024)
- [Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting](https://mlsanity.com/paper/2112.00958/) (2021)
- [TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network](https://mlsanity.com/paper/2208.08768/) (2022)
- [FullFormer: Generating Shapes Inside Shapes](https://mlsanity.com/paper/2303.11235/) (2023)
- [Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction](https://mlsanity.com/paper/2404.19075/) (2024)
- [Neural Adaptive SCEne Tracing](https://mlsanity.com/paper/2202.13664/) (2022)
- [TouchAnything: Diffusion-Guided 3D Reconstruction from Sparse Robot Touches](https://mlsanity.com/paper/2604.08945/) (2026)
- [Convolutional Neural Network-based Efficient Dense Point Cloud Generation using Unsigned Distance Fields](https://mlsanity.com/paper/2203.11537/) (2022)
- [On Optimal Sampling for Learning SDF Using MLPs Equipped with Positional Encoding](https://mlsanity.com/paper/2401.01391/) (2024)
- [SurfR: Surface Reconstruction with Multi-scale Attention](https://mlsanity.com/paper/2506.08635/) (2025)
- [GLVD: Guided Learned Vertex Descent](https://mlsanity.com/paper/2510.06046/) (2025)
- [BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions](https://mlsanity.com/paper/2601.13445/) (2026)

## Abstract (arXiv)

Prior work to infer 3D texture use either texture atlases, which require uv-mappings and hence have discontinuities, or colored voxels, which are memory inefficient and limited in resolution. Recent work, predicts RGB color at every XYZ coordinate forming a texture field, but focus on completing texture given a single 2D image. Instead, we focus on 3D texture and geometry completion from partial and incomplete 3D scans. IF-Nets have recently achieved state-of-the-art results on 3D geometry completion using a multi-scale deep feature encoding, but the outputs lack texture. In this work, we generalize IF-Nets to texture completion from partial textured scans of humans and arbitrary objects. Our key insight is that 3D texture completion benefits from incorporating local and global deep features extracted from both the 3D partial texture and completed geometry. Specifically, given the partial 3D texture and the 3D geometry completed with IF-Nets, our model successfully in-paints the missing texture parts in consistence with the completed geometry. Our model won the SHARP ECCV'20 challenge, achieving highest performance on all challenges.