Highly Articulated Gaussian Human Avatars with Textured Mesh Prior

核心概念

A novel approach for generating photo-realistic and animatable human avatars from monocular input videos by learning a joint representation using Gaussian splatting and textured mesh.

摘要

The paper presents a novel method called HAHA (Highly Articulated Gaussian Human Avatars with Textured Mesh Prior) for generating animatable human avatars from monocular input videos. The key idea is to learn a joint representation using Gaussian splatting and textured mesh, where the textured mesh is used to represent the body surface and Gaussians are used to capture out-of-mesh details like hair and clothing.

The method consists of three stages:

1. In the first stage, a full Gaussian representation of the avatar is learned by optimizing the Gaussian parameters and fine-tuning the SMPL-X pose and shape.
2. In the second stage, a textured mesh representation of the avatar is learned by optimizing the texture while keeping the SMPL-X parameters fixed.
3. In the final stage, the Gaussian and textured mesh representations are merged, and an unsupervised method is used to remove unnecessary Gaussians by learning their opacity.

The authors demonstrate that HAHA can achieve reconstruction quality on par with state-of-the-art methods on the SnapshotPeople dataset while using significantly fewer Gaussians (up to 3 times fewer). They also show that HAHA outperforms previous methods on the more challenging X-Humans dataset, both quantitatively and qualitatively, especially in handling highly articulated body parts like fingers.

The key contributions of the work are:

1. The use of a joint representation with Gaussians and textured mesh to increase the efficiency of rendering human avatars.
2. An unsupervised method for significantly reducing the number of Gaussians in the scene through the use of a textured mesh.
3. The ability to efficiently handle the animation of highly articulated body parts like hands without any additional engineering.

統計資料

The paper does not contain any key metrics or important figures to support the author's key logics.

引述

The paper does not contain any striking quotes supporting the author's key logics.

從以下內容提煉的關鍵洞見

HAHA

by David Svitov... 於 arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01053.pdf

深入探究

How can the proposed method be extended to handle more complex clothing and accessories beyond just hair and loose clothing

To extend the proposed method to handle more complex clothing and accessories beyond just hair and loose clothing, several strategies can be implemented:

Enhanced Texture Mapping: Implement more detailed texture mapping techniques to capture intricate patterns and textures of clothing and accessories. This can involve using high-resolution textures and advanced mapping algorithms to accurately represent complex clothing designs.

Dynamic Mesh Deformation: Introduce dynamic mesh deformation techniques to simulate the movement and interaction of clothing and accessories with the underlying body. This can involve physics-based simulations or data-driven approaches to realistically animate clothing and accessories.

Layered Representation: Implement a layered representation approach where different components of clothing and accessories are modeled separately and then combined in the rendering process. This allows for individual control and manipulation of each layer, enhancing the realism of the final avatar.

Customizable Templates: Develop customizable templates or templates with adjustable parameters for different types of clothing and accessories. This would allow users to tailor the appearance of the avatars based on their preferences or specific design requirements.

By incorporating these strategies, the method can be extended to handle a wider range of clothing and accessories, providing more realistic and detailed avatars in the rendering process.

What are the potential limitations of the textured mesh representation, and how can they be addressed to further improve the quality of the generated avatars

The potential limitations of the textured mesh representation include:

Lack of Fine Details: Textured mesh representations may struggle to capture fine details and intricate patterns present in clothing and accessories, leading to a loss of realism in the generated avatars.

Limited Flexibility: Textured mesh representations may have limitations in terms of flexibility and adaptability to different body shapes and poses, potentially resulting in distortions or inaccuracies in the rendered avatars.

To address these limitations and further improve the quality of the generated avatars, the following approaches can be considered:

Advanced Texture Mapping: Implement advanced texture mapping techniques such as neural texture rendering to enhance the level of detail and realism in the textured mesh representation.

Dynamic Texture Deformation: Introduce dynamic texture deformation methods to simulate the movement and deformation of textures on the mesh surface, allowing for more realistic rendering of clothing and accessories.

Texture Synthesis: Explore texture synthesis algorithms to generate high-quality textures that can be seamlessly applied to the mesh surface, improving the overall visual quality of the avatars.

By incorporating these approaches, the limitations of the textured mesh representation can be mitigated, leading to more realistic and visually appealing avatars.

How can the method be adapted to work with other parametric human body models beyond SMPL-X, and what are the implications of using different models

Adapting the method to work with other parametric human body models beyond SMPL-X involves:

Model Compatibility: Ensure that the method is compatible with the specific parameterization and structure of the alternative human body model. This may require adjustments in the representation of joints, articulation, and other anatomical features.

Data Mapping: Develop a mapping mechanism to translate the input data (such as monocular videos) to the parameter space of the new human body model. This mapping should account for differences in the model's topology and parameterization.

Training Adaptation: Fine-tune the training process to optimize the model parameters specific to the new human body model. This may involve retraining certain components of the method to better align with the characteristics of the alternative model.

Evaluation and Validation: Conduct thorough evaluation and validation to ensure that the adapted method produces accurate and realistic avatars with the new human body model. This includes testing for pose accuracy, shape fidelity, and animation quality.

By addressing these considerations, the method can be successfully adapted to work with other parametric human body models, expanding its applicability and versatility in generating high-quality avatars.