Morphable Diffusion: Enabling 3D-Consistent and Controllable Novel View Synthesis of Humans from a Single Image

Conceitos essenciais

The proposed morphable diffusion model enables the generation of 3D-consistent and controllable photorealistic human avatars from a single input image by integrating a 3D morphable model into a state-of-the-art multi-view diffusion framework.

Resumo

The paper introduces a novel "morphable diffusion" model that enhances the state-of-the-art multi-view consistent diffusion approach for the task of creating controllable, photorealistic human avatars. The key idea is to integrate a 3D morphable model into the diffusion pipeline, which allows for accurate conditioning of the generative process on the articulated 3D model. This integration facilitates seamless and accurate incorporation of facial expression and body pose control into the generation process.

The paper first analyzes the applicability of existing multi-view consistent diffusion models for human avatar creation and shows that a simple fine-tuning on limited datasets leads to sub-optimal results. To address this, the proposed morphable diffusion model leverages a well-studied statistical model of human shapes (e.g. SMPL, FLAME) to introduce human priors and guide the reconstruction process.

The model takes a single input image and an underlying morphable model as input, and generates N multi-view consistent novel views. It does this by unprojecting and interpolating the noisy image features onto the mesh vertices, processing them through a sparse 3D ConvNet, and then using a 2D UNet conditioned on the 3D-aware feature volume to predict the denoised images.

The paper further proposes an efficient training scheme that disentangles the reconstruction (guided by the input image) and the animation (guided by the morphable model), enabling the generation of new facial expressions for an unseen subject from a single image.

Extensive quantitative and qualitative evaluations demonstrate the advantages of the proposed morphable diffusion model over existing state-of-the-art avatar creation methods on both novel view and novel expression synthesis tasks.

Estatísticas

"Given a single input image (a) and a morphable mesh model with a target facial expression (b) our method directly generates 3D consistent and photo-realistic images from novel viewpoints (c)."
"Using the generated multi-view consistent images, we can reconstruct a coarse 3D model (d) using off-the-shelf neural surface reconstruction methods such as [87]."

Citações

"To the best of our knowledge, our proposed framework is the first diffusion model to enable the creation of fully 3D-consistent, animatable, and photorealistic human avatars from a single image of an unseen subject."
"Our key idea is to leverage a well-studied statistical model of human shapes [42, 50] to introduce human prior and guide the reconstruction process. More importantly, a controllable 3D model also allows us to perform a more challenging task of photorealistic animation of a human head from a single image under novel expression (Figure 1)."

Principais Insights Extraídos De

Morphable Diffusion

by Xiyi Chen,Ma... às arxiv.org 04-03-2024

https://arxiv.org/pdf/2401.04728.pdf

Perguntas Mais Profundas

How can the proposed morphable diffusion model be extended to handle a wider range of human appearances, including diverse ethnicities and hairstyles, beyond the limitations of the training dataset

To extend the proposed morphable diffusion model to handle a wider range of human appearances, including diverse ethnicities and hairstyles, several strategies can be implemented:

Dataset Augmentation: By incorporating more diverse datasets that include a wide range of ethnicities, hairstyles, and clothing styles, the model can learn to generalize better across different appearances. This will expose the model to a more comprehensive set of features and variations present in real-world scenarios.

Data Balancing: Ensuring a balanced representation of different ethnicities and hairstyles in the training data can help prevent biases and improve the model's ability to generate accurate representations of individuals from various backgrounds.

Transfer Learning: Pre-training the model on a more diverse dataset that includes a broader range of human appearances can provide a strong foundation for fine-tuning on specific datasets with limited diversity. This transfer learning approach can help the model adapt to new appearances more effectively.

Style Transfer Techniques: Leveraging style transfer techniques can enable the model to learn to adapt features such as hairstyles, facial features, and clothing styles from one individual to another. This can enhance the model's ability to generate diverse human avatars.

Adversarial Training: Incorporating adversarial training techniques can help the model learn to generate realistic and diverse human appearances by training against discriminators that evaluate the authenticity of the generated images across different ethnicities and hairstyles.

How can the model's generalization to arbitrary camera viewpoints be improved, reducing the reliance on external 3D reconstruction methods for free-path novel view synthesis

Improving the model's generalization to arbitrary camera viewpoints for free-path novel view synthesis can be achieved through the following methods:

Incorporating Camera Pose Information: By training the model with a more diverse set of camera poses during the training phase, the model can learn to generalize better to arbitrary viewpoints. This exposure to a wide range of camera poses can help the model adapt to novel viewpoints more effectively.

Augmented Training Data: Augmenting the training data with images captured from various camera viewpoints can help the model learn to synthesize images from different perspectives. This augmentation can enhance the model's ability to handle arbitrary camera viewpoints during novel view synthesis.

Multi-View Consistency Loss: Incorporating a multi-view consistency loss during training can encourage the model to generate images that are consistent across different viewpoints. This constraint can help improve the model's generalization to arbitrary camera viewpoints.

Fine-Tuning with Diverse Camera Data: Fine-tuning the model with additional data captured from diverse camera viewpoints can help the model adapt to new perspectives and improve its performance in free-path novel view synthesis tasks.

What other applications beyond avatar creation could benefit from the integration of 3D morphable models into diffusion-based generative frameworks

The integration of 3D morphable models into diffusion-based generative frameworks can benefit various applications beyond avatar creation, including:

Virtual Try-On: By incorporating 3D morphable models into generative frameworks, virtual try-on applications can be enhanced. Users can visualize how clothing items fit and look on their personalized 3D avatars, improving the online shopping experience.

Virtual Makeup Try-On: The integration of 3D morphable models can enable realistic virtual makeup try-on experiences. Users can experiment with different makeup styles and products on their digital avatars before making purchasing decisions.

Facial Expression Analysis: Generative frameworks utilizing 3D morphable models can aid in facial expression analysis applications. By generating realistic facial expressions on 3D avatars, these models can be used for emotion recognition, virtual communication, and facial animation.

Personalized Content Creation: Content creators can leverage 3D morphable models in generative frameworks to create personalized and customizable digital content. This can include personalized animations, virtual characters, and interactive experiences tailored to individual preferences.