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Avatar: A Large Animatable Reconstruction Model for High-Fidelity 3D Head Avatars
0:00:02 Introduction: Avatar is introduced as a large, animatable reconstruction model capable of creating high-fidelity 3D head avatars.
0:00:06 Input: The method predicts avatars from just four images in a single forward pass, eliminating the need for complex fitting or test-time optimization.
0:00:15 Reconstruction: From four casual phone-captured images, Avatar performs a sparse 3D head reconstruction.
0:00:23 Animatable Representation: The resulting 3D GAN representation is animatable, allowing for full control over facial expressions.
0:00:30 Animation Speed Generated Avatars can be driven and animated at interactive frame rates.
0:00:34 Generalization: The model can realistically animate and "in-paint" unseen regions, even with person-specific facial expressions it hasn't encountered before.
0:00:44 Process Time: The entire process, from phone capture to avatar generation, takes approximately 2 minutes.
0:00:52 Technical Overview: Avatar takes four posed images as input and predicts a 3D Gaussian for each pixel.
0:01:00 Feature Extraction: Position maps (from DUSTER) and feature maps (from SAPIENS) are used for each input view.
0:01:07 Vision Transformer: A Vision Transformer backend processes these inputs to infer 3D structure.
0:01:11 Facial Motion: Facial motion is modeled using cross-attention to a sequenced expression code.
0:01:16 Skip Connections: Skip connections are added to predicted position and color maps to simplify the model's task.
0:01:22 Gaussian Fusion: 3D Gaussians with sufficient confidence are fused to create the final 3D head.
0:01:30 Mesh Independence: The design doesn't rely on a template mesh, allowing for accurate modeling of complex features like long hair and accessories.
0:01:39 Training Data: Avatar is trained using the AVA-256 dataset, a large multi-view video dataset of human heads, requiring four input images and one supervision image with a corresponding expression code.
0:01:51 Comparison with GP Avatar: Avatar generates avatars that better resemble the target person and are free from temporal flickering compared to GP Avatar.
0:02:05 Generalization to Rare Appearances: The model successfully reconstructs features like blue hair and headbands, which GP Avatar struggles with.
0:02:14 Generalization to New Datasets: Avatar can create realistic avatars from the NeuRend dataset, which was not used in training and has different lighting, viewpoints, and camera properties.
0:02:32 Ablation Study: Removing DUSTER position maps and SAPIENS feature maps results in misaligned and blurry results. SAPIENS maps improve sharpness, while DUSTER maps enhance detail alignment.
0:02:51 Input Consistency: Inconsistent input expressions lead to severe misalignments. The full method produces sharp, aligned renderings even with varying input expressions.
0:03:11 Single Image Input: Avatar can work with a single image by using a pre-trained 3D GAN to perform 3D lifting and rendering the four required input views.
0:03:30 Single-Shot Avatars: Even with single-image input, the method produces highly realistic, animatable avatars.
0:03:56 Comparision with GAN Avatar: When working with a single input image, Avatar is fully view consistent and create more realistic expressions.
0:04:06 Applications: Avatar can animate heads randomly sampled from a 3D GAN, make any static 3D GAN animatable and also generalizes beyond real faces.
0:04:22 Animation Capabilities Extend: Avatar can animate out-of-distribution inputs, such as antique busts.