{"id":15,"date":"2025-05-09T20:33:40","date_gmt":"2025-05-09T20:33:40","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2025team11\/?page_id=15"},"modified":"2025-12-12T22:04:39","modified_gmt":"2025-12-12T22:04:39","slug":"method","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2025team11\/method\/","title":{"rendered":"Backgrounds & Related Works"},"content":{"rendered":"\n

Animatable 3D Head Avatars<\/h2>\n\n\n\n

Traditional high-quality avatars required expensive multi-view stereo setups, specialized equipment, and manual crafting. Recent approaches have shifted towards neural implicit functions (NeRFs) and volumetric representations, which are more accessible but often computationally heavy.<\/p>\n\n\n

\n
\"\"<\/figure>\n<\/div>\n\n\n

Most recently, 3D Gaussian Splatting (3DGS)<\/strong> has revolutionized the field with real-time rendering speeds. While methods like GaussianAvatars<\/em> bind Gaussians to parametric meshes, they rely heavily on the quality of the input video. If the source is blurry, the resulting avatar lacks definition\u2014a problem SuperHead specifically targets.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

3D Super-Resolution<\/h2>\n\n\n\n

Enhancing Fidelity Beyond 2D<\/strong><\/p>\n\n\n\n

Current 3D super-resolution techniques often rely on applying 2D super-resolution (SR) models to rendered images frame-by-frame<\/sup>.<\/p>\n\n\n\n

The Problem:<\/strong> This naive approach often leads to temporal flickering and inconsistency across different viewing angles. Video-based SR methods offer better stability but still struggle with ensuring long-range consistency under large pose changes.<\/p>\n\n\n\n

Our Edge:<\/strong> SuperHead enforces multi-view and temporal consistency directly in the 3D space, rather than just post-processing 2D outputs.<\/p>\n\n\n\n

Generative Priors & GAN Inversion<\/h2>\n\n\n\n

Leveraging Pre-trained Knowledge<\/strong><\/p>\n\n\n\n

3D-aware GANs (e.g., GSGAN<\/em>, EG3D<\/em>) have demonstrated incredible ability to synthesize photorealistic 3D heads from limited data by “inverting” images into a latent space<\/sup>.<\/p>\n\n\n\n

While previous works have used GAN inversion for static face reconstruction or editing<\/sup><\/sup><\/sup><\/sup>, applying it to super-resolve dynamic, animatable avatars<\/strong> remains unexplored. SuperHead bridges this gap by combining the rich details of 3D GAN priors with the flexibility of rigorous mesh-based animation<\/sup><\/sup><\/sup><\/sup>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Animatable 3D Head Avatars Traditional high-quality avatars required expensive multi-view stereo setups, specialized equipment, and manual crafting. Recent approaches have shifted towards neural implicit functions (NeRFs) and volumetric representations, which are more accessible but often computationally heavy. Most recently, 3D Gaussian Splatting (3DGS) has revolutionized the field with real-time rendering speeds. While methods like GaussianAvatars … <\/p>\n

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