3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping (2212.07378v2)

Abstract: We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photorealistic images of full-body humans with consistent appearances under different view-angles and body-poses. To tackle the representational and computational challenges in synthesizing the articulated structure of human bodies, we propose a novel generator architecture in which a 2D convolutional backbone is modulated by a 3D pose mapping network. The 3D pose mapping network is formulated as a renderable implicit function conditioned on a posed 3D human mesh. This design has several merits: i) it leverages the strength of 2D GANs to produce high-quality images; ii) it generates consistent images under varying view-angles and poses; iii) the model can incorporate the 3D human prior and enable pose conditioning. Project page: https://3dhumangan.github.io/.

• The paper introduces a hybrid generator architecture that fuses a 2D convolutional backbone with a 3D pose mapping network for realistic full-body human image synthesis.
• It leverages a renderable implicit function and a segmentation-based GAN loss to ensure consistent appearances across varying poses and viewpoints.
• The model achieves competitive FID and PSNR scores, offering practical benefits for augmented reality, digital media production, and next-generation avatar creation.

3DHumanGAN: Advancements in 3D-Aware Human Image Generation

The paper presents 3DHumanGAN, a sophisticated generative adversarial network designed for synthesizing full-body human images that maintain consistent appearances across varying view-angles and body-poses. This work addresses significant challenges in rendering articulated human body structures by introducing an innovative generator architecture that leverages a hybrid approach combining 2D and 3D representations. The tool achieves a delicate balance between computational efficiency and high-resolution output, embodying a nuanced understanding of the strengths endemic to both 2D and 3D generative models.

The architectural innovation in 3DHumanGAN lies in its generator design, where a 2D convolutional backbone is modulated by a 3D pose mapping network. By utilizing a renderable implicit function conditioned by a posed 3D human mesh, the system offers several strategic advantages: it accesses the rendering precision typical of 2D GANs, sustains appearance consistency notwithstanding changes in view or pose, and incorporates 3D human priors to enable accurate pose conditioning. Notably, this model employs an adversarial learning mechanism from a curated set of web-based images, obviating the necessity for extensive manual annotations.

In tackling human image generation—a domain marked by challenges akin to those of natural image synthesis and necessitating high levels of fidelity—the research positions itself within the contemporary landscape of 3D-aware GAN developments. Prior methods' attempts to manage complex, articulated objects witnessed constraints, often faltering when computational demands elevated beyond memory-efficient capacities. In contrast, 3DHumanGAN introduces a highly efficient hybrid generator, where low-dimensional 3D geometric information computed through pose mapping translates seamlessly into detailed 2D textures.

Through quantitative metrics, including Frechet Inception Distance (FID) and Peak Signal-to-Noise Ratio (PSNR), 3DHumanGAN demonstrates competitive performance, establishing its robustness in synthesizing visually plausible human figures that are pose and view-consistent. The architectural deployment of a $1 \times 1$ convolutional strategy within the generator aids in mitigating issues related to inconsistency under geometric transformations, a persistent challenge in conventional CNN-based pipelines.

From the perspective of supervision, the paper innovates with its segmentation-based GAN loss, encouraging the alignment of synthesized 2D semantics with their 3D geometrical underpinnings. The fusion of this approach with a loss-based perceptual analysis places the research in a distinct category, showcasing an enhanced capacity for true-to-form synthesis far exceeding prior methods constrained by either 3D rendering constraints or simplistic 2D loss paradigms.

The implications of 3DHumanGAN are manifold. Practically, the model offers significant potential across digital media production, from augmented reality applications to next-generation avatar creation for online platforms. Theoretically, this work underpins future explorations in hybrid generative models, providing a compelling case for further aligning 3D geometric understanding with 2D visual synthesis frameworks. It paves the way for continued inquiry into how 3D priors can be synergistically integrated into GAN architectures, refining both efficiency and qualitative outputs.

The paper leaves open avenues for future research to expand generalizability, particularly in adapting to novel poses and extrinsic views unseen in training data. Moreover, attention could be directed towards addressing incremental but still existing inconsistencies in appearance, especially under extreme view shifts. In advancing computational modeling practices, incorporating efficient scene-specific 3D representations stands out as a promising direction.

In summation, 3DHumanGAN not only represents a significant stride in human image generation, rooted in a thoughtful confluence of 2D and 3D paradigms, but it also accentuates new methodologies for delivering tailored, high-fidelity, and computationally optimized synthetic imagery across ever-diversifying application domains.