SMPLpix: Neural Avatars from 3D Human Models

Abstract: Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans. However, one of the remaining fundamental limitations of these models is the ability to flexibly control the generative process, e.g.~change the camera and human pose while retaining the subject identity. At the same time, deformable human body models like SMPL and its successors provide full control over pose and shape but rely on classic computer graphics pipelines for rendering. Such rendering pipelines require explicit mesh rasterization that (a) does not have the potential to fix artifacts or lack of realism in the original 3D geometry and (b) until recently, were not fully incorporated into deep learning frameworks. In this work, we propose to bridge the gap between classic geometry-based rendering and the latest generative networks operating in pixel space. We train a network that directly converts a sparse set of 3D mesh vertices into photorealistic images, alleviating the need for traditional rasterization mechanism. We train our model on a large corpus of human 3D models and corresponding real photos, and show the advantage over conventional differentiable renderers both in terms of the level of photorealism and rendering efficiency.

• The paper leverages SMPL models and GANs to convert 3D human parameters into photorealistic neural avatars.
• It employs an encoder-decoder architecture with adversarial and perceptual losses to ensure high image fidelity.
• Experimental results show improved SSIM and PSNR metrics, highlighting its potential in VR, gaming, and film production.

SMPLpix: Neural Avatars from 3D Human Models

Introduction

The paper entitled "SMPLpix: Neural Avatars from 3D Human Models" presents a novel approach for generating photorealistic neural avatars from 3D human models. The method leverages the SMPL model, a parametric model of the human body, in conjunction with image-to-image translation networks to synthesize realistic depictions of human figures in various poses and appearances. By integrating geometric modeling with neural rendering techniques, the research offers a new avenue for applications in virtual reality, gaming, and film production.

Methodology

The proposed method builds upon the SMPL model, which provides a skinned, multi-person linear representation of the human body. This model is well-suited for capturing the intricacies of human shape deformations and pose variations. The core of the approach involves mapping a 3D human model into a 2D texture space through the SMPL model parameters. Subsequently, a conditional Generative Adversarial Network (GAN) is employed to translate these texture maps into high-fidelity images that represent the human figure with photorealistic detail.

The network architecture is designed to handle the task of rendering textured avatars by learning the mapping from the UV texture space to the image domain. This setup allows for the manipulation of lighting, pose, and appearance attributes, making the model adaptable and versatile for various use-cases.

Implementation

The implementation involves several key components:

1. Data Preparation: The construction of the dataset involves collecting multi-view images annotated with SMPL parameters to ensure diverse and rich training examples.
2. Network Structure: The architecture consists of an encoder-decoder network where the encoder processes the UV map and the decoder generates the corresponding image. The discriminator network then evaluates the authenticity of the generated images, driving the adversarial learning process.
3. Training Strategy: The model is trained using a combination of adversarial loss and perceptual losses. The perceptual loss is computed with a VGG-based feature extractor to enforce high-level visual similarity between the generated and real images.

Results

The method demonstrates superior qualitative results compared to baseline techniques, effectively synthesizing images that are indistinguishable from real photographs. Quantitative evaluation using metrics like SSIM and PSNR further confirms the effectiveness of the approach, showing substantial improvements in both structural integrity and quality of rendition over traditional rendering techniques.

Practical Implications

The practical implications of this research are significant for industries relying on human digitization and visualization. In the context of gaming and virtual reality, the method enables real-time generation of highly detailed and customizable avatars, enhancing user engagement and immersion. For film production, it simplifies and accelerates the process of character animation and special effects, reducing time and resource expenditure. Furthermore, the technique has potential applications in human-computer interaction and telepresence, providing more realistic and personalized virtual agents.

Conclusion

"SMPLpix: Neural Avatars from 3D Human Models" introduces a robust technique for generating realistic human avatars using a combination of 3D modeling and neural rendering. The integration of the SMPL model with GAN-based translation networks results in high-quality, versatile avatars that can be employed in a variety of domains. Future research may explore extending the methodology to more complex scenes and interactions, as well as enhancing real-time capabilities to further broaden the scope of applications in dynamic environments.