While high fidelity and efficiency are central to the creation of digital head avatars, recent methods relying on 2D or 3D generative models often experience limitations such as shape distortion, expression inaccuracy, and identity flickering.
The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene.
The problem of modeling an animatable 3D human head avatar under light-weight setups is of significant importance but has not been well solved.
We introduce Control4D, an innovative framework for editing dynamic 4D portraits using text instructions.
Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods.
Compared with existing works, we obtain superior novel view synthesis results and faithfully face reenactment performance.
We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
At its core is a novel diffusion-based stereo module, which introduces diffusion models, a type of powerful generative models, into the iterative stereo matching network.
Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility.
With the rise of big data technologies, many smart transportation applications have been rapidly developed in recent years including bus arrival time predictions.