RegHead: Non-Humanoid Head Blendshapes via Feed-Forward Registration
RegHead generates non-humanoid head blendshapes via feed-forward registration, faster than optimization methods.
Key Findings
Methodology
RegHead constructs a large-scale dataset of non-humanoid identities, employs a dense stochastic anchor motion representation, and uses a feed-forward registration model to convert unregistered expression meshes into a corresponded blendshape basis. The model combines a global matcher and a local matcher to ensure efficient expression mesh generation without ground-truth.
Key Results
- RegHead produces higher-fidelity expression meshes than baseline methods and runs orders of magnitude faster than optimization methods.
- In experiments, RegHead outperforms ActionMesh and V2M4 in PSNR, SSIM, and LPIPS metrics.
- Achieves real-time retargeting from human face tracking signals to non-humanoid characters, capturing head pose and localized facial motions.
Significance
RegHead is significant in both academia and industry, addressing the high-cost issue of generating non-humanoid character blendshapes. The method not only accelerates generation speed but also improves quality, offering a more efficient solution for character animation in AR communication, social media, and games.
Technical Contribution
RegHead's technical contributions include the introduction of dense stochastic anchor motion representation and a feed-forward registration model, avoiding the high computational cost of traditional optimization methods. The method trains without ground-truth, providing new theoretical guarantees and engineering possibilities.
Novelty
RegHead is the first to apply feed-forward registration to non-humanoid character blendshape generation, differing from existing methods by using a topology-agnostic motion representation without predefined skeletons or templates.
Limitations
- RegHead may perform poorly with extremely complex facial topologies, especially when anchor layouts are not dense enough.
- The model relies on high-quality initial datasets, and dataset quality directly affects the final results.
Future Work
Future work could include expanding dataset diversity, improving model performance on extremely complex topologies, and exploring more real-time application scenarios.
AI Executive Summary
RegHead introduces a novel framework for generating animatable non-humanoid head blendshapes. Traditional methods face high costs and inefficiencies in generating these blendshapes, while RegHead significantly improves generation speed and quality by constructing a large-scale dataset and using a feed-forward registration model.
The core technologies of RegHead include dense stochastic anchor motion representation and a feed-forward registration model. The method predicts anchor deformations from the neutral shape to convert unregistered expression meshes into a corresponded blendshape basis. Experimental results show that RegHead outperforms existing baseline methods in both fidelity and speed.
RegHead's successful application in real-time retargeting, from human face tracking signals to non-humanoid characters, captures head poses and localized facial motions. This breakthrough offers a more efficient solution for character animation in AR communication, social media, and games. Nonetheless, RegHead still has room for improvement in handling extremely complex facial topologies.
Deep Analysis
Background
With the development of AR communication, social media, and games, generating animatable non-humanoid head characters has become increasingly important. However, existing methods face high costs and inefficiencies in generating these characters' blendshapes. Traditional methods rely on manual artist adjustments or heavy optimization processes, making large-scale applications difficult.
Core Problem
Generating non-humanoid character blendshapes requires solving several bottlenecks, including the lack of consistent expression supervision, the typical lack of correspondence in generated 4D assets, and highly localized facial motion. These issues make existing methods perform poorly when handling diverse non-humanoid characters.
Innovation
RegHead's core innovations include introducing dense stochastic anchor motion representation and a feed-forward registration model. By constructing a large-scale non-humanoid character dataset, RegHead can train models without ground-truth and achieve efficient expression mesh generation by predicting anchor deformations.
Methodology
- �� Construct a large-scale non-humanoid character dataset to provide consistent semantic expression observations.
- �� Define dense stochastic anchor motion representation to accommodate localized facial deformations.
- �� Use a feed-forward registration model to convert unregistered expression meshes into a corresponded blendshape basis.
- �� Combine a global matcher and a local matcher to ensure efficient expression mesh generation.
Experiments
Experiments used a dataset of approximately 20k non-humanoid identities, each with a fixed semantic expression set. The model trains without ground-truth using differentiable rendering loss and geometric loss. Results show RegHead outperforms existing baseline methods in PSNR, SSIM, and LPIPS metrics.
Results
RegHead outperforms existing baseline methods in both fidelity and speed. Experimental results show RegHead outperforms ActionMesh and V2M4 in PSNR, SSIM, and LPIPS metrics, running orders of magnitude faster than optimization methods.
Applications
RegHead can be used in character animation for AR communication, social media, and games. Its efficient blendshape generation method enables real-time retargeting, capturing head poses and localized facial motions from face tracking signals.
Limitations & Outlook
RegHead may perform poorly with extremely complex facial topologies, especially when anchor layouts are not dense enough. Additionally, the model relies on high-quality initial datasets, and dataset quality directly affects the final results. Future work could include expanding dataset diversity and improving model performance on extremely complex topologies.
Plain Language Accessible to non-experts
Imagine you are playing a role-playing game and need to design various expressions for your character. Traditional methods are like manually crafting a mask for each expression, which is very time-consuming. RegHead is like a smart mask-making machine that can quickly generate masks for various expressions by inputting basic character information. This is because RegHead uses a technique called 'feed-forward registration,' which can quickly predict changes in the character's face and generate corresponding expressions. It's like having a smart assistant that helps you quickly complete character design.
ELI14 Explained like you're 14
Imagine you're playing Minecraft and want to add some cool expressions to your character. Traditional methods are like manually carving a mask for each expression, super tedious. RegHead is like a magic tool that automatically generates expressions when you tell it what you want. This is because RegHead has a super smart algorithm that can quickly predict changes in the character's face and generate corresponding expressions. So you can easily add various expressions to your character and have more fun playing!
Glossary
Feed-Forward Registration
A method for quickly predicting deformations of unregistered meshes without per-instance optimization.
Used to convert unregistered expression meshes into a corresponded blendshape basis.
Blendshapes
Low-dimensional expression representations for animation, supporting cross-identity retargeting.
Provides an interpretable animation interface.
Dense Stochastic Anchors
A topology-agnostic motion representation for localized facial deformations.
Avoids the use of predefined skeletons or templates.
Differentiable Rendering Loss
A point-based rendering loss used to supervise deformations.
Used to optimize the model's training objective.
Chamfer Distance
A metric for measuring the distance between two sets of points.
Used to supervise the geometric deformation of the global matcher.
Open Questions Unanswered questions from this research
- 1 How to improve RegHead's performance with extremely complex facial topologies? Existing methods may perform poorly when anchor layouts are not dense enough.
- 2 How to further reduce reliance on high-quality initial datasets? Dataset quality directly affects the final results.
Applications
Immediate Applications
AR Communication
RegHead can be used for character animation in augmented reality, providing efficient blendshape generation.
Social Media
In social media, RegHead can be used to generate personalized virtual character expressions, enhancing user interaction experience.
Long-term Vision
Game Development
RegHead can be used in game character design, providing a more efficient expression generation tool, reducing development time.
Abstract
We present RegHead, a framework for constructing semantic blendshape sets for animatable non-humanoid head avatars. With a fixed expression vocabulary, semantic blendshapes provide a low-dimensional and interpretable animation interface and support cross-identity retargeting. Building such blendshape sets remains expensive because (i) expression-consistent supervision is scarce, (ii) generated 4D assets typically lack correspondence, and (iii) facial motion is highly localized. We propose (1) a large-scale dataset of non-humanoid identities paired with a shared expression vocabulary, obtained by expanding a small artist-rigged library via fine-tuned image editing; (2) a dense stochastic anchor motion representation tailored to localized facial deformations; and (3) a fast feed-forward registration model that converts unregistered expression meshes into a corresponded blendshape basis by predicting anchor-based deformations from the neutral shape. Experiments show that our approach produces higher-fidelity expression meshes than baselines, while running orders of magnitude faster than optimization. We further demonstrate real-time retargeting from human face tracking signals to non-humanoid characters, capturing both head pose and localized facial motions. Our project page is available at https://snap-research.github.io/RegHead/.