{"id":94,"date":"2025-12-12T04:32:59","date_gmt":"2025-12-12T04:32:59","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2025team11\/?page_id=94"},"modified":"2025-12-12T21:07:11","modified_gmt":"2025-12-12T21:07:11","slug":"methodology","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2025team11\/methodology\/","title":{"rendered":"Methodology"},"content":{"rendered":"\n

Overview<\/strong><\/h2>\n\n\n\n

Given a low-resolution 3D head avatar driven by a morphable model, our pipeline operates in three main stages. We first reconstruct a static 3D head in the canonical space with multi-view 3D GAN inversion<\/strong>. We then refine mesh geometry<\/strong> and rig 3D Gaussians onto the mesh surface to enable animation. Finally, we include anchor images with diverse camera poses and expressions for dynamics-aware 3D refinement<\/strong>, ensuring the robustness of the 3D head model across viewing angles and complex facial motions.<\/p>\n\n\n\n

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

Key Components<\/strong><\/h2>\n\n\n\n

To achieve this, we break down the process into three core technical modules:<\/p>\n\n\n\n

1. Multi-View 3D Inversion (Canonical Reconstruction)<\/strong> The first step is to hallucinate missing high-frequency details from the low-quality input.<\/p>\n\n\n\n

The Process:<\/strong> We utilize a pre-trained 3D GAN to generate a static, high-resolution 3D Gaussian head. By optimizing the latent code based on upscaled multi-view renderings, we ensure the reconstructed head possesses photorealistic textures and geometry.
Why it matters:<\/strong> This establishes a high-fidelity “base model” that far exceeds the quality of the original blurry input.<\/p>\n\n\n\n

2. 3D Gaussian Rigging & Geometry Refinement<\/strong> A high-quality static head must be rigged correctly to move convincingly.<\/p>\n\n\n\n

Geometry Refinement:<\/strong> Low-resolution inputs often suffer from misalignment (e.g., teeth not aligning with lips). We refine the underlying FLAME mesh geometry to strictly align with facial landmarks before binding.
Rigging:<\/strong> The optimized 3D Gaussians are then bound to this refined mesh, allowing the detailed textures to follow the face’s movement naturally.<\/p>\n\n\n\n

3. Dynamics-Aware 3D Refinement<\/strong> Standard inversion often fails when the face deforms into extreme expressions.<\/p>\n\n\n\n

Multi-Expression Anchors:<\/strong> We sample “anchor images” containing diverse expressions (such as open mouths or squints) to capture occluded regions like teeth and eyelids.
Joint Optimization:<\/strong> We jointly optimize the model using these dynamic anchors. This ensures that the super-resolved avatar maintains its identity and geometric consistency not just in a neutral pose, but across all complex facial motions.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Overview Given a low-resolution 3D head avatar driven by a morphable model, our pipeline operates in three main stages. We first reconstruct a static 3D head in the canonical space with multi-view 3D GAN inversion. We then refine mesh geometry and rig 3D Gaussians onto the mesh surface to enable animation. Finally, we include anchor … <\/p>\n

Continue reading “Methodology”<\/span><\/a><\/p>\n","protected":false},"author":259,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-94","page","type-page","status-publish","hentry"],"yoast_head":"\nMethodology - 3D Gaussian Human Enhancement<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team11\/methodology\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Methodology - 3D Gaussian Human Enhancement\" \/>\n<meta property=\"og:description\" content=\"Overview Given a low-resolution 3D head avatar driven by a morphable model, our pipeline operates in three main stages. 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