Instant 3D Human Avatar Generation using Image Diffusion Models (2406.07516v2)

Abstract: We present AvatarPopUp, a method for fast, high quality 3D human avatar generation from different input modalities, such as images and text prompts and with control over the generated pose and shape. The common theme is the use of diffusion-based image generation networks that are specialized for each particular task, followed by a 3D lifting network. We purposefully decouple the generation from the 3D modeling which allow us to leverage powerful image synthesis priors, trained on billions of text-image pairs. We fine-tune latent diffusion networks with additional image conditioning for image generation and back-view prediction, and to support qualitatively different multiple 3D hypotheses. Our partial fine-tuning approach allows to adapt the networks for each task without inducing catastrophic forgetting. In our experiments, we demonstrate that our method produces accurate, high-quality 3D avatars with diverse appearance that respect the multimodal text, image, and body control signals. Our approach can produce a 3D model in as few as 2 seconds, a four orders of magnitude speedup wrt the vast majority of existing methods, most of which solve only a subset of our tasks, and with fewer controls. AvatarPopUp enables applications that require the controlled 3D generation of human avatars at scale. The project website can be found at https://www.nikoskolot.com/avatarpopup/.

• The paper introduces AvatarPopUp, a novel two-stage method combining diffusion-based image generation and 3D lifting to generate high-fidelity 3D human avatars instantly while enabling multifaceted control.
• The paper demonstrates that leveraging fine-tuned latent diffusion models with a pixel-aligned 3D reconstruction network achieves a four orders of magnitude speedup and comparable or superior reconstruction metrics.
• The paper highlights applications in gaming, virtual reality, and digital fashion, showcasing robust performance in generating customizable and photorealistic avatars from diverse inputs.

Instant 3D Human Avatar Generation using Image Diffusion Models

The paper "Instant 3D Human Avatar Generation using Image Diffusion Models" by Kolotouros et al., introduces a novel methodology termed AvatarPopUp, which addresses the challenge of rapid, high-quality 3D human avatar generation from diverse input modalities like images and text prompts. The method distinctively integrates the strengths of diffusion-based image generation networks with a subsequent 3D lifting network, thus achieving remarkable efficiency and control in avatar creation.

This essay provides an expert overview of the contribution, key methodologies, experimental results, and implications of this research work.

Contribution and Methodology

AvatarPopUp is designed around a two-stage decoupled process: the initial stage leverages pretrained text-to-image generative networks to produce high-fidelity 2D images based on user-defined text, poses, and shapes; the subsequent stage employs a feed-forward neural network for 3D reconstruction from these 2D images. This decoupling allows the exploitation of large-scale 2D datasets, circumventing the limitation of scarce 3D training data.

Key aspects of the methodology include:

1. Fine-tuned Latent Diffusion Networks: These networks are employed to generate diverse and detailed front and back views of humans from textual descriptions and pose/shape encodings without detrimental overfitting. The latent diffusion networks are fine-tuned on extensive multimodal datasets, incorporating both synthetic and real-world examples.
2. 3D Reconstruction Network: Utilizing a convolutional encoder that computes pixel-aligned feature maps from the generated images, the method predicts a 3D shape and texture using a signed distance field representation. The result is a textured 3D mesh inferred from 2D front and back images, preserving geometric and textural details with minimal ambiguity.
3. Control and Hypothesis Generation: AvatarPopUp offers multifaceted control over the avatar generation process, including adjustments for body pose, shape, and appearance, promoting diverse hypotheses generation. This granular control is a significant improvement over prior art which lacked such comprehensive configurability.

Experimental Results

The paper substantiates its claims through rigorous experimentation:

• Speed and Efficiency: AvatarPopUp generates a 3D model in 2 to 10 seconds, demonstrating a four orders of magnitude speedup compared to traditional optimization-based methods which are substantially slower, taking minutes to hours per instance.
• Numerical Evaluation: The efficacy of AvatarPopUp was validated using metrics such as Chamfer distance, Normal Consistency, and Volume IoU for 3D reconstruction accuracy, as well as qualitative evaluations against state-of-the-art approaches. AvatarPopUp consistently showed superior or comparable performance, notably excelling in metrics for both detailed geometric reconstruction and photorealistic texture generation.
• Applications: Multiple use cases are highlighted, including 3D avatar generation from text prompts, single-image 3D reconstruction, and virtual try-on capabilities. The system demonstrated robust performance in diverse scenarios, emphasizing flexibility and precision.

Implications and Future Directions

The practical implications of this research are multifaceted:

• Scalability in Digital Human Representation: The ability to rapidly generate high-quality avatars has notable applications in gaming, virtual reality, and social media, where personalized avatars enhance user engagement and experience.
• Animation and Virtual Try-On: The methodology's inherent support for animated and editable avatars addresses the needs of industries focused on digital fashion, entertainment, and education, enabling real-time virtual try-on and character animation.

From a theoretical perspective, the decoupled approach of AvatarPopUp is a significant contribution, demonstrating how separate but complementary expert systems can be integrated to overcome the limitations of large-scale 3D data scarcity. This strategy is extendable to other domains requiring complex multi-modal generative models.

Future research could explore alternative 3D lifting strategies beyond pixel-aligned features and expand the dataset diversity further to include more varied and challenging real-world scenarios. Additionally, refining the control mechanisms could lead to even more nuanced and user-customizable avatar generation.

Conclusion

AvatarPopUp represents a significant advance in the field of 3D avatar generation, marked by its remarkable efficiency, extensive control options, and high fidelity in output. The research presents a robust case for the adoption of diffusion-based networks coupled with 3D lifting techniques, setting a precedent for future work in scalable and interactive 3D human modeling. Through this contribution, Kolotouros et al. pave the way for innovative applications across multiple industries, potentially transforming how digital human avatars are created and utilized.