# Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior

arXiv: https://arxiv.org/abs/2503.01610 · HTML: https://mlsanity.com/paper/2503.01610/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 85 of its 95 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 30 of 85)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Volume Rendering of Neural Implicit Surfaces](https://mlsanity.com/paper/2106.12052/) (2021)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video](https://mlsanity.com/paper/2201.04127/) (2022)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [Animatable Neural Radiance Fields for Modeling Dynamic Human Bodies](https://mlsanity.com/paper/2105.02872/) (2021)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (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)
- [Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies](https://mlsanity.com/paper/1801.01615/) (2018)
- [A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose](https://mlsanity.com/paper/2102.06199/) (2021)
- [PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction](https://mlsanity.com/paper/2007.03858/) (2020)
- [Sapiens: Foundation for Human Vision Models](https://mlsanity.com/paper/2408.12569/) (2024)
- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [Neural Articulated Radiance Field](https://mlsanity.com/paper/2104.03110/) (2021)
- [3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting](https://mlsanity.com/paper/2312.09228/) (2023)
- [GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians](https://mlsanity.com/paper/2312.02134/) (2023)
- [ARCH++: Animation-Ready Clothed Human Reconstruction Revisited](https://mlsanity.com/paper/2108.07845/) (2021)
- [TAVA: Template-free Animatable Volumetric Actors](https://mlsanity.com/paper/2206.08929/) (2022)
- [SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting](https://mlsanity.com/paper/2403.05087/) (2024)
- [GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis](https://mlsanity.com/paper/2312.02155/) (2023)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)

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

- [CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction](https://mlsanity.com/paper/2512.11988/) (2025)
- [UP2You: Fast Reconstruction of Yourself from Unconstrained Photo Collections](https://mlsanity.com/paper/2509.24817/) (2025)
- [HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars](https://mlsanity.com/paper/2507.19481/) (2025)
- [LHM++: An Efficient Large Human Reconstruction Model for Pose-free Images to 3D](https://mlsanity.com/paper/2506.13766/) (2025)
- [GASPACHO: Gaussian Splatting for Controllable Humans and Objects](https://mlsanity.com/paper/2503.09342/) (2025)
- [GarmentX: Autoregressive Parametric Representations for High-Fidelity 3D Garment Generation](https://mlsanity.com/paper/2504.20409/) (2025)
- [Parametric Gaussian Human Model: Generalizable Prior for Efficient and Realistic Human Avatar Modeling](https://mlsanity.com/paper/2506.06645/) (2025)
- [MonoCloth: Reconstruction and Animation of Cloth-Decoupled Human Avatars from Monocular Videos](https://mlsanity.com/paper/2508.04505/) (2025)
- [Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining](https://mlsanity.com/paper/2604.02320/) (2026)
- [DNF-Avatar: Distilling Neural Fields for Real-time Animatable Avatar Relighting](https://mlsanity.com/paper/2504.10486/) (2025)
- [PHD: Personalized 3D Human Body Fitting with Point Diffusion](https://mlsanity.com/paper/2508.21257/) (2025)
- [HART: Human Aligned Reconstruction Transformer](https://mlsanity.com/paper/2509.26621/) (2025)
- [Gaussian Wardrobe: Compositional 3D Gaussian Avatars for Free-Form Virtual Try-On](https://mlsanity.com/paper/2603.04290/) (2026)
- [AHOY! Animatable Humans under Occlusion from YouTube Videos with Gaussian Splatting and Video Diffusion Priors](https://mlsanity.com/paper/2603.17975/) (2026)
- [High-Fidelity Mobile Avatars with Pruned Local Blendshapes](https://mlsanity.com/paper/2605.01854/) (2026)
- [DAMA: Disentangled Body-Anchored Gaussians for Controllable Multi-Layered Avatars](https://mlsanity.com/paper/2605.21001/) (2026)
- [End-to-End Spatial-Temporal Transformer for Real-time 4D HOI Reconstruction](https://mlsanity.com/paper/2603.14435/) (2026)
- [ShowMak3r: Compositional TV Show Reconstruction](https://mlsanity.com/paper/2504.19584/) (2025)
- [ReMu: Reconstructing Multi-layer 3D Clothed Human from Image Layers](https://mlsanity.com/paper/2508.01381/) (2025)
- [JOintGS: Joint Optimization of Cameras, Bodies and 3D Gaussians for In-the-Wild Monocular Reconstruction](https://mlsanity.com/paper/2602.04317/) (2026)
- [Spatio-Temporal Garment Reconstruction Using Diffusion Mapping via Pattern Coordinates](https://mlsanity.com/paper/2602.24043/) (2026)
- [RHINO: Reconstructing Human Interactions with Novel Objects from Monocular Videos](https://mlsanity.com/paper/2605.17014/) (2026)
- [PiG-Avatar: Hierarchical Neural-Field-Guided Gaussian Avatars](https://mlsanity.com/paper/2605.20185/) (2026)
- [H-Flow: Self-supervised Human Scene Flow via Physics-inspired Joint Multi-modal Learning](https://mlsanity.com/paper/2605.22629/) (2026)
- [Generator-Refiner-Examiner: A Tri-Module Data Augmentation Framework for 3D Human Avatar Learning from Monocular Videos](https://mlsanity.com/paper/2605.23555/) (2026)
- [AvatarMix: Identity-Preserving Cross-Avatar Composition for Outfit Personalization](https://mlsanity.com/paper/2606.03506/) (2026)
- [JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video](https://mlsanity.com/paper/2606.31115/) (2026)
- [LUNA: Learning Universal 3D Human Animation Beyond Skinning](https://mlsanity.com/paper/2606.31981/) (2026)
- [FlexiAvatar: Unified 3D Gaussian Human Avatars Under Arbitrary Body Visibility](https://mlsanity.com/paper/2607.19100/) (2026)
- [AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures](https://mlsanity.com/paper/2608.19900/) (2026)

## Abstract (arXiv)

We present Vid2Avatar-Pro, a method to create photorealistic and animatable 3D human avatars from monocular in-the-wild videos. Building a high-quality avatar that supports animation with diverse poses from a monocular video is challenging because the observation of pose diversity and view points is inherently limited. The lack of pose variations typically leads to poor generalization to novel poses, and avatars can easily overfit to limited input view points, producing artifacts and distortions from other views. In this work, we address these limitations by leveraging a universal prior model (UPM) learned from a large corpus of multi-view clothed human performance capture data. We build our representation on top of expressive 3D Gaussians with canonical front and back maps shared across identities. Once the UPM is learned to accurately reproduce the large-scale multi-view human images, we fine-tune the model with an in-the-wild video via inverse rendering to obtain a personalized photorealistic human avatar that can be faithfully animated to novel human motions and rendered from novel views. The experiments show that our approach based on the learned universal prior sets a new state-of-the-art in monocular avatar reconstruction by substantially outperforming existing approaches relying only on heuristic regularization or a shape prior of minimally clothed bodies (e.g., SMPL) on publicly available datasets.