Deformable NeRF With 3DMM Guidance for Facial Pose Editing
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Solution Overview
Problem
Conventional neural radiance field (NeRF) networks lack intuitive control over object deformations, particularly facial poses and expressions, and fail to accurately capture non-rigid dynamics and background geometry, limiting their editing capabilities.
Innovation Solution
A deformable NeRF scene representation model using a 3D morphable model (3DMM) guided deformation field, refined by a multilayer perceptron (MLP), enables explicit control over facial appearance and camera position, capturing non-rigid dynamics and accessories, allowing for accurate editing of facial poses and expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional NeRF networks are used to generate 3D scenes from 2D images, then view synthesis capability is achieved, but control over object deformations and facial expressions is lost
Solution Approach 1:
The deformation field is segmented into two independent components: a 3DMM-based component that captures structured facial geometry and expressions, and a residual component that captures unmodeled deformations. This segmentation allows explicit control over facial expressions through 3DMM parameters while maintaining the ability to represent complex non-rigid dynamics through the residual field.
Solution Approach 2:
The 3D morphable face model (3DMM) serves as an intermediary between the input images and the NeRF representation. It provides a structured intermediate representation that enables explicit control over facial geometry and expressions, which then guides the deformation field to achieve controllable 3D scene editing.
2Reliability
If conventional NeRF networks are used, then 3D scene reconstruction is achieved, but accurate capture of non-rigid dynamics is limited
Solution Approach 1:
The deformation field is segmented into a 3DMM-based component that captures structured facial geometry and expressions, and a residual component that captures unmodeled deformations. This segmentation allows the model to focus computational resources on accurately representing non-rigid dynamics where they matter most, while using the structured 3DMM to handle regular facial variations.
Solution Approach 2:
The deformation field is constructed as a composite of two different representations: the 3DMM-based deformation field that provides geometric accuracy for facial structures, and the residual deformation field that captures complex non-rigid dynamics. This composite approach leverages the strengths of both representations to achieve accurate non-rigid dynamics capture.
3Ease of operation
If conventional NeRF networks are used, then 3D scene generation is achieved, but intuitive control over head poses is not possible
Solution Approach 1:
The 3DMM serves as an intermediary that provides explicit parametric control over head poses. By representing the head geometry in the 3DMM space with controllable pose parameters, the system enables intuitive pose control while maintaining accurate 3D geometric representation through the guided deformation field.
Solution Approach 2:
The model enables pose control by changing the pose parameters in the 3DMM representation. These parameter changes are then propagated through the guided deformation field to generate the corresponding deformed 3D scene, providing both ease of operation through parameter manipulation and precision through the structured 3DMM model.
4Adaptability or versatility
If conventional NeRF networks are used, then 3D scene rendering is achieved, but editing capabilities are limited
Solution Approach 1:
The deformation field is segmented into controllable components based on the 3DMM representation. This segmentation enables independent editing of different facial attributes (expressions, poses, geometry) by manipulating the corresponding 3DMM parameters, while the guided deformation field ensures that edits are applied with high spatial precision to the correct regions of the 3D scene.
Data Source
AI summary
A scene modeling system receives a video including a plurality of frames corresponding to views of an object and a request to display an editable three-dimensional (3D) scene that corresponds to a particular frame of the plurality of frames. The scene modeling system applies a scene representation model to the particular frame, and includes a deformation model configured to generate, for each pixel of the particular frame based on a pose and an expression of the object, a deformation point using a 3D morphable model (3DMM) guided deformation field. The scene representation model includes a color model configured to determine, for the deformation point, color and volume density values. The scene modeling system receives a modification to one or more of the pose or the expression of the object including a modification to a location of the deformation point and renders an updated video based on the received modification.