Joint2Human: Efficient Generation of High-Quality 3D Humans with Global Structure and Local Details

Core Concepts

Joint2Human leverages 2D diffusion models to efficiently generate high-quality 3D humans with reasonable global structure and fine-grained geometry details, enabled by a compact spherical embedding of 3D joints, a high-frequency enhancer, and a multi-view recarving strategy.

Abstract

Joint2Human proposes a novel method for directly generating detailed 3D human geometry using 2D diffusion models. The key components include:

Latent Diffusion for Fourier Occupancy Fields (FOF): Joint2Human utilizes a VAE to compress the high-dimensional FOF data into a lower-dimensional latent space, which is then used to train a diffusion model for efficient 3D human generation.

Compact Spherical Embedding of 3D Joints: To enable flexible pose control, Joint2Human introduces a compact spherical embedding of 3D human joints, which integrates full semantic and depth-wise information for precise pose guidance during generation.

High-Frequency Enhancer: To recover the missing high-frequency details in the low-frequency FOF representation, Joint2Human learns a reference-based decoding network to predict the high-frequency terms and enhance the geometric details.

Multi-View Recarving Strategy: To address the artifacts along the direction orthogonal to the normal direction of the generated shape, Joint2Human performs a multi-view recarving process on the 3D space and fuses the occupancy fields from different views.

Experimental results demonstrate that Joint2Human outperforms state-of-the-art methods in terms of global structure, local details, and computational efficiency. The method also exhibits versatility by enabling text-guided 3D human generation.

Stats

The FOF feature maps have at least 32 channels to represent and generate high-quality 3D human geometry.
The total number of time steps for the diffusion model is set as T = 1000, T' = 200.

Quotes

"To efficiently generate high-quality 3D humans with reasonable global structure and fine-grained geometry details, in this paper, we propose Joint2Human, a conditional generative network with 2D diffusion models derived from 3D datasets."
"We propose a new pose guidance embedding, a compact spherical embedding of 3D human joints, for efficient perception of global structure. This mechanism also facilitates a more straightforward and effective implementation of pose-guided generation in 2D generation framework."
"We design a high-frequency enhancer by integrating a subsidiary decoder into the pre-trained VAE and a multi-view recarving strategy for fine-grained local detail generation. Both of them improve the geometry quality of the final results."

Key Insights Distilled From

Joint2Human

by Muxin Zhang,... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2312.08591.pdf

Deeper Inquiries

How can the proposed compact spherical embedding of 3D joints be extended to handle more complex human poses, such as extreme or unusual poses

The proposed compact spherical embedding of 3D joints can be extended to handle more complex human poses by incorporating advanced techniques for pose estimation and representation. One approach could involve integrating hierarchical joint representations to capture finer details and nuances in poses. By utilizing a multi-level embedding structure, the model can better understand the relationships between joints and their movements in extreme poses. Additionally, incorporating temporal information from motion sequences can help in predicting and generating poses that are not explicitly present in the training data. This temporal modeling can enable the system to anticipate and generate poses beyond the standard range, including unusual or extreme poses. Furthermore, leveraging reinforcement learning techniques to guide the generation process towards achieving specific pose objectives can enhance the model's ability to handle complex poses effectively.

What are the potential limitations of the text-guided 3D human generation approach, and how could it be further improved to handle a wider range of textual descriptions

The text-guided 3D human generation approach may face limitations in accurately interpreting and translating a wide range of textual descriptions into realistic 3D human models. One potential limitation is the ambiguity and variability in textual descriptions, which can lead to challenges in generating precise and consistent 3D representations. To address this, the approach could be further improved by incorporating a more robust text understanding module that can parse and interpret complex textual descriptions effectively. This module could leverage natural language processing techniques, such as pre-trained language models, to enhance the model's ability to comprehend diverse textual inputs. Additionally, integrating a feedback mechanism that allows users to provide corrections or additional information during the generation process can help refine the output based on user input. By enhancing the text understanding capabilities and incorporating user feedback mechanisms, the system can improve its accuracy and flexibility in generating 3D human models from textual descriptions.

Given the advancements in 3D human generation, how might this technology be applied in emerging fields like virtual avatars, digital twins, or mixed reality applications, and what ethical considerations should be addressed

The advancements in 3D human generation technology have significant implications for emerging fields such as virtual avatars, digital twins, and mixed reality applications. In the context of virtual avatars, the ability to generate high-quality and customizable 3D human models can enhance user experiences in virtual environments, gaming, and social interactions. Digital twins, which are virtual representations of physical objects or systems, can benefit from 3D human generation by creating realistic human avatars for simulation, training, and monitoring purposes. Mixed reality applications, combining virtual and real-world elements, can leverage 3D human generation for creating immersive experiences, interactive storytelling, and personalized content delivery.
However, the application of 3D human generation technology also raises ethical considerations related to privacy, consent, and representation. Ensuring the ethical use of generated 3D human models involves obtaining consent from individuals whose likeness is being used, protecting personal data, and preventing misuse or misrepresentation of generated avatars. Additionally, addressing issues of bias, diversity, and inclusivity in 3D human generation algorithms is crucial to avoid perpetuating stereotypes or underrepresentation of certain groups. Implementing transparent and accountable practices in data collection, model training, and deployment can help mitigate ethical concerns and promote responsible use of 3D human generation technology.