Introduction
Have you ever marveled at the incredible power of generating lifelike and expressive 3D avatars with just a few simple words? If so, then let me introduce you to DreamHuman, an innovative deep learning model developed by Google Research. It harnesses the potential of natural language descriptions to create animatable 3D human models.
DreamHuman is the product of a collaborative effort between esteemed researchers from Google Research. Fueled by the desire to craft diverse and personalized 3D characters for a myriad of applications such as gaming, animation, virtual reality, and social media, this team embarked on a quest to design a model that seamlessly integrates the richness of natural language to generate high-quality 3D avatars, effortlessly adaptable and customizable.
What is DreamHuman?
DreamHuman is a generative model that materializes 3D human models from textual descriptions. Its versatility spans a broad spectrum of inputs, encompassing names, occupations, hobbies, emotions, poses, clothing styles, colors, accessories, and facial features. DreamHuman conjures up a vivid 3D avatar that faithfully reflects the given description.
The foundation of DreamHuman lies in the concept of conditional variational autoencoders (CVAEs). These neural networks possess the remarkable ability to learn and generate data samples based on specific conditions. In the case of DreamHuman, the condition is the textual input, while the data sample is the intricate 3D human model. This cutting-edge model consists of two key components: a text encoder and a 3D decoder. The text encoder seamlessly converts the textual input into a latent vector, capturing the essence of its semantic meaning. Subsequently, the 3D decoder utilizes this vector to fabricate a comprehensive 3D human model, incorporating shape, texture, and pose into a cohesive whole.
Key Features of DreamHuman
DreamHuman sets itself apart with an array of remarkable features that revolutionize the way 3D human models are generated. Let's explore some of the key attributes that make DreamHuman truly exceptional:
- Text-to-Shape: DreamHuman possesses the ability to transform text inputs into lifelike and diverse 3D human shapes. It effortlessly handles intricate details, such as body proportions, facial expressions, hairstyles, and accessories, ensuring a captivating outcome. Moreover, it goes beyond the limitations of training data by seamlessly interpolating between different latent vectors to produce novel shapes.
- Text-to-Texture: With DreamHuman, the realm of possibilities expands further as it generates realistic and diverse 3D human textures from text inputs. It seamlessly navigates through various clothing styles, colors, patterns, and materials, resulting in visually stunning outcomes. By skillfully blending and mixing different texture components, DreamHuman creates textures that are entirely fresh and innovative.
- Text-to-Pose: DreamHuman empowers you to bring your imagination to life by generating a myriad of realistic and diverse 3D human poses from text inputs. Whether it's a poised stance, a seated position, a graceful dance, a dynamic run, or a mid-air jump, DreamHuman handles an array of poses effortlessly. By expertly blending different pose components, it even creates poses that defy the boundaries of existing training data.
- Animatability: DreamHuman takes customization to new heights by producing 3D human models that are not only realistic but also highly animatable. Manipulating and personalizing these models is a breeze, thanks to their compatibility with standard animation software and tools like Blender and Unity. Users can effortlessly modify the shape, texture, pose, or expression of the models using simple text commands or intuitive sliders.
Capabilities/Use Cases of DreamHuman
DreamHuman opens up a world of possibilities across a multitude of domains and scenarios, offering limitless potential. Let's delve into some of the captivating applications and use cases:
- Gaming: Embrace the power of DreamHuman to breathe life into your games. Game developers and players alike can now create a diverse cast of 3D characters, personalized to their desires. By utilizing natural language, they can effortlessly define attributes and preferences for their avatars. Additionally, animating these avatars is a breeze with a vast library of predefined or custom motions.
- Animation: Unleash your creative vision with DreamHuman's unparalleled capabilities. Animators and artists can now fashion realistic and expressive 3D characters with ease. Describing the appearance and personality of characters becomes effortless through natural language inputs. Furthermore, animating these characters is a seamless process, utilizing standard or custom rigs.
- Virtual Reality: Step into a world of immersive experiences with DreamHuman. VR users and developers can now create lifelike and interactive 3D environments populated by expressive human agents. Through natural language commands, different types of human models can be effortlessly generated to suit various scenarios and tasks. Interacting with these models becomes a truly immersive experience, whether through voice commands or gestures.
- Social Media: Transform your social media presence with DreamHuman's captivating 3D content. Social media users and influencers can now create unique and engaging human models tailored for their platforms. Using natural language, they can effortlessly generate different types of human models to suit diverse purposes and occasions. Sharing these models with followers and friends adds an extra layer of personalization and creativity to their online presence.
DreamHuman unleashes a new era of 3D human modeling, where imagination knows no bounds. With its exceptional features and boundless applications.
How does DreamHuman operate?
The shape module generates the 3D representation of human anatomy based on the latent vector. It leverages a statistical human body model (SMPL-X) to ensure realistic and consistent body proportions and topology. Additionally, it acquires knowledge of instance-specific deformations, enabling the capture of intricate details like facial expressions, hairstyles, and accessories. Consequently, the shape module produces a mesh depiction of the 3D human structure.
Moving on, the texture module fabricates the 3D texture of the human model using both the latent vector and the mesh representation. By relying on a texture atlas, it guarantees a seamless and coherent texture mapping. Furthermore, it acquires expertise in instance-specific texture blending, accommodating various clothing styles, colors, patterns, and materials. As a result, the texture module generates a texture map for the mesh representation.
Lastly, the pose module generates the 3D human pose based on the latent vector and the mesh representation. It utilizes a kinematic skeleton as a prior, ensuring realistic and consistent joint angles and orientations. Moreover, it develops proficiency in instance-specific pose blending, facilitating a wide range of poses such as standing, sitting, dancing, running, and jumping. Consequently, the pose module outputs a posed mesh representation of the 3D human model.
The ultimate outcome of DreamHuman is a neural radiance field, which encompasses information about the color and density of each point within the 3D environment. This neural radiance field can be rendered from any viewpoint using conventional ray tracing techniques. Moreover, by manipulating the pose of the 3D human model through simple text commands or sliders, the neural radiance field can also be animated.
How to access and use this model?
DreamHuman is a research project that is not yet publicly available as a code or a system. However, the researchers have published their paper and their website where they provide more details and results of their work. They also provide an avatar gallery and an animation gallery where you can see some examples of the 3D human models generated by DreamHuman from different text inputs.
DreamHuman is not open-source or commercially usable at the moment. The researchers state that they plan to release their code and data in the future, but they do not specify a timeline or a licensing structure. They also acknowledge that their work raises ethical and social issues, such as privacy, consent, and representation, and they encourage further discussion and research on these topics.
If you are interested to know more about DreamHuman, all relevant links are provided under the 'source' section at the end of this article.
Limitations
While DreamHuman is an impressive and innovative model that can generate realistic and animatable 3D human models from text inputs, it does have certain limitations and drawbacks that require attention and improvement in the future. Here are a few of them:
- Resolution: One limitation of using a text-to-image diffusion model for supervision is its input resolution of 64×64 pixels. Consequently, textures often appear blurry, and the model lacks intricate details in its geometry. To enhance the quality and fidelity of the generated models, the researchers propose exploring higher-resolution text-to-image models or alternative sources of supervision.
- Diversity: Another limitation pertains to the diversity and coverage of both the text inputs and the 3D human models. The researchers rely on a dataset of 10,000 text prompts obtained from Amazon Mechanical Turk workers, which might not encompass the full range of possible variations and combinations of human attributes and descriptions. Additionally, the dataset of 3D human scans is sourced from various places, potentially lacking representation of the entire spectrum of human appearance, clothing, skin tones, and body shapes. Acknowledging the presence of biases and limitations inherent in the data, the researchers advocate for more extensive efforts in collecting diverse and inclusive datasets for text-to-3D generation.
- Generalization: A third limitation revolves around the model's ability to generalize and remain robust when confronted with unseen or novel text inputs. The researchers assert that their model can handle complex and nuanced attributes, including facial expressions, hairstyles, accessories, clothing styles, colors, patterns, and materials. However, they also acknowledge that the model might encounter challenges or produce artifacts when exposed to ambiguous, contradictory, or out-of-distribution text inputs. To enhance the generalization and robustness, the researchers propose incorporating additional prior knowledge or constraints into the model.
Conclusion
DreamHuman is a remarkable achievement in text-to-3D generation that opens up new possibilities and challenges for creating realistic and expressive 3D human avatars from natural language descriptions. It is a powerful tool for professional artists and 3D animators as well as casual users who want to create unique and engaging 3D content for various purposes. It is also a fascinating research topic that invites more exploration and innovation in computer vision, natural language processing, computer graphics, machine learning, ethics, sociology etc.
source
https://arxiv.org/abs/2306.09329
https://dream-human.github.io/
https://dream-human.github.io/animation_gallery.html
https://dream-human.github.io/avatar_gallery.html
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