# SuperFace: Preference-Aligned Facial Expression Estimation Beyond Pseudo Supervision

arXiv: https://arxiv.org/abs/2605.06179 · HTML: https://mlsanity.com/paper/2605.06179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 7 May 2026.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
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- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization](https://mlsanity.com/paper/2503.12937/) (2025)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [A Compact Embedding for Facial Expression Similarity](https://mlsanity.com/paper/1811.11283/) (2018)
- [ExpNet: Landmark-Free, Deep, 3D Facial Expressions](https://mlsanity.com/paper/1802.00542/) (2018)
- [Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction](https://mlsanity.com/paper/2505.00615/) (2025)
- [FaceLLM: A Multimodal Large Language Model for Face Understanding](https://mlsanity.com/paper/2507.10300/) (2025)
- [Ig3D: Integrating 3D Face Representations in Facial Expression Inference](https://mlsanity.com/paper/2408.16907/) (2024)
- [FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning](https://mlsanity.com/paper/2505.13419/) (2025)
- [Multimodal Prompt Alignment for Facial Expression Recognition](https://mlsanity.com/paper/2506.21017/) (2025)
- [Express4D: Expressive, Friendly, and Extensible 4D Facial Motion Generation Benchmark](https://mlsanity.com/paper/2508.12438/) (2025)
- [SemanticFace: Semantic Facial Action Estimation via Semantic Distillation in Interpretable Space](https://mlsanity.com/paper/2603.14827/) (2026)
- [Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction](https://mlsanity.com/paper/2309.05782/) (2023)

## Abstract (arXiv)

Accurate facial estimation is crucial for realistic digital human animation, and ARKit blendshape coefficients offer an interpretable representation by mapping facial motions to semantic animation controls. However, learning high-quality ARKit coefficient prediction remains limited by the absence of reliable ground-truth supervision. Existing methods typically rely on capture software such as Live Link Face to provide pseudo labels, which may contain noisy activations, biased coefficient magnitudes, and missing or inaccurate facial actions. Consequently, models trained with supervised learning tend to reproduce imperfect pseudo labels rather than optimize for perceptual expression fidelity. In this paper, we propose SuperFace, a preference-driven framework that moves ARKit facial expression estimation from pseudo-label imitation toward human-aligned perceptual optimization. Instead of treating software-estimated coefficients as fixed ground truth, SuperFace uses them only as an initialization and further improves coefficient prediction through human preference feedback on rendered facial expressions. By aligning the model with perceptual judgments rather than numerical pseudo labels, SuperFace enables more visually faithful and expressive facial animation. Experiments show that SuperFace improves expression fidelity over Live Link Face supervision, demonstrating the effectiveness of preference-driven optimization for semantic facial action prediction.